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| 1,041,153,631
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I_kwDODunzps4-Dr5f
| 3,190
|
combination of shuffle and filter results in a bug
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[
"I cannot reproduce this on master and pyarrow==4.0.1.\r\n",
"Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13\r\n\r\nCan you try to update `datasets` and try again ?",
"Thanks a lot, fixes with 1.13"
] |
2021-11-01T13:07:29Z
|
2021-11-02T10:50:49Z
|
2021-11-02T10:50:49Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
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| 1,041,044,986
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I_kwDODunzps4-DRX6
| 3,189
|
conll2003 incorrect label explanation
|
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[
"Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value\r\n```\r\n\r\n"
] |
2021-11-01T11:03:30Z
|
2021-11-09T10:40:58Z
|
2021-11-09T10:40:58Z
|
CONTRIBUTOR
| null | null | null | null |
In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging.
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I_kwDODunzps4-DBro
| 3,188
|
conll2002 issues
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[
"Hi ! Thanks for reporting :)\r\n\r\nThis is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.\r\n",
"Ah, hadn't seen that sorry.\r\n\r\nThe scrambled \"point of contact\" is a separate issue though, I think.",
"@lhoestq The \"point of contact\" is still an issue.",
"It will be fixed in https://github.com/huggingface/datasets/pull/3274, thanks"
] |
2021-11-01T09:49:24Z
|
2021-11-15T13:50:59Z
|
2021-11-12T17:18:11Z
|
CONTRIBUTOR
| null | null | null | null |
**Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did
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"It's an issue with the streaming mode:\r\n\r\n```python\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('nli_tr', name='snli_tr',split='test', streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/nli_tr/c2ddd0c0a70caddac6a81c2dae5ca7939f00060d517d08f1983927818dba6521/nli_tr.py\", line 155, in _generate_examples\r\n with codecs.open(filepath, encoding=\"utf-8\") as f:\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/codecs.py\", line 905, in open\r\n file = builtins.open(filename, mode, buffering)\r\nFileNotFoundError: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_test.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip'\r\n```\r\n\r\nNote that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. `nli_tr` is above the limit, hence the error.",
"cc @huggingface/datasets ",
"Apparently there is an issue with the data source URLs: Server Not Found\r\n- https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip\r\n\r\nWe are contacting the authors to ask them: \r\n@e-budur you are one of the authors: are you aware of the issue with the URLs of your data ?",
"Reported to their repo:\r\n- https://github.com/boun-tabi/NLI-TR/issues/9",
"The server issue was temporary and is now resolved.",
"Once we have implemented support for streaming, the viewer works: https://huggingface.co/datasets/nli_tr"
] |
2021-10-31T03:56:33Z
|
2022-09-12T09:15:34Z
|
2022-09-12T08:43:09Z
|
CONTRIBUTOR
| null | null | null | null |
## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
|
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I_kwDODunzps4-AZh5
| 3,185
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7z dataset preview not implemented?
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[
"It's a bug in the dataset viewer: the dataset cannot be downloaded in streaming mode, but since the dataset is relatively small, the dataset viewer should have fallback to normal mode. Working on a fix.",
"Fixed. https://huggingface.co/datasets/samsum/viewer/samsum/train\r\n\r\n<img width=\"1563\" alt=\"Capture d’écran 2022-04-12 à 13 47 45\" src=\"https://user-images.githubusercontent.com/1676121/162953339-cd8922d7-9037-408b-b896-eac1af0bb54f.png\">\r\n\r\nThanks for reporting!"
] |
2021-10-30T20:18:27Z
|
2022-04-12T11:48:16Z
|
2022-04-12T11:48:07Z
|
NONE
| null | null | null | null |
## Dataset viewer issue for dataset 'samsum'
**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
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I_kwDODunzps49-Eox
| 3,181
|
`None` converted to `"None"` when loading a dataset
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[
"Hi @eladsegal, thanks for reporting.\r\n\r\n@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.\r\n\r\nAll values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value(\"bool\")`, `None` is casted to `False`.\r\n\r\nIt is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).",
"Thanks for reporting.\r\n\r\nThis is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?\r\n\r\nEDIT: the other types (bool, int, etc) can also become nullable IMO",
"So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.\r\nUsing the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.",
"Hi @eladsegal,\r\n\r\nUse `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e\r\n```\r\n\r\nI'm making all the feature types nullable as we speak, and the fix will be merged probably early next week.",
"Hi @mariosasko, is there an estimation as to when this issue will be fixed?",
"https://github.com/huggingface/datasets/pull/3195 fixed it, we'll do a new release soon :)\r\n\r\nFor now feel free to install `datasets` from the master branch",
"Thanks, but unfortunately looks like it isn't fixed yet 😢 \r\n[notebook for 1.14.0](https://colab.research.google.com/drive/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)\r\n[notebook for master](https://colab.research.google.com/drive/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing)",
"Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick.",
"Thank you, it works! 🎊 "
] |
2021-10-29T15:23:53Z
|
2021-12-11T01:16:40Z
|
2021-12-09T14:26:57Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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I_kwDODunzps499pvY
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Cannot load dataset when the config name is "special"
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"The issue is that the datasets are malformed. Not a bug with the datasets library"
] |
2021-10-29T13:30:47Z
|
2021-10-29T13:35:21Z
|
2021-10-29T13:35:21Z
|
COLLABORATOR
| null | null | null | null |
## Describe the bug
After https://github.com/huggingface/datasets/pull/3159, we can get the config name of "Check/region_1", which is "Check___region_1".
But now we cannot load the dataset (not sure it's related to the above PR though). It's the case for all the similar datasets, listed in https://github.com/huggingface/datasets-preview-backend/issues/78
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_config_names
>>> get_dataset_config_names("Check/region_1")
['Check___region_1']
>>> load_dataset("Check/region_1")
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
Downloading and preparing dataset json/Check___region_1 to /home/slesage/.cache/huggingface/datasets/json/Check___region_1-d2b3bc48f11c9be2/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4443.12it/s]
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Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "builder_name" does not exist in table schema'
```
Loading in streaming mode also returns something strange:
```python
>>> list(load_dataset("Check/region_1", streaming=True, split="train"))
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
[{'builder_name': None, 'citation': '', 'config_name': None, 'dataset_size': None, 'description': '', 'download_checksums': None, 'download_size': None, 'features': {'speech': {'feature': {'dtype': 'float64', 'id': None, '_type': 'Value'}, 'length': -1, 'id': None, '_type': 'Sequence'}, 'sampling_rate': {'dtype': 'int64', 'id': None, '_type': 'Value'}, 'label': {'dtype': 'string', 'id': None, '_type': 'Value'}}, 'homepage': '', 'license': '', 'post_processed': None, 'post_processing_size': None, 'size_in_bytes': None, 'splits': None, 'supervised_keys': None, 'task_templates': None, 'version': None}, {'_data_files': [{'filename': 'dataset.arrow'}], '_fingerprint': 'f1702bb5533c549c', '_format_columns': ['speech', 'sampling_rate', 'label'], '_format_kwargs': {}, '_format_type': None, '_indexes': {}, '_indices_data_files': None, '_output_all_columns': False, '_split': None}]
```
## Expected results
The dataset should be loaded
## Actual results
An error occurs
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Linux-5.11.0-1020-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
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"Property couldn't be hashed properly" even though fully picklable
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[
"After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:\r\n\r\n> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().\r\n\r\nIn the utils, this is explicitly enabled\r\n\r\nhttps://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330\r\n\r\nIs this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)",
"Hi ! Thanks for reporting\r\n\r\nYes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function\r\n\r\nEDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210)",
"I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. ",
"@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.\r\n\r\n```python\r\nfrom functools import partial\r\nfrom pathlib import Path\r\n\r\nimport spacy\r\nfrom datasets import Dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n lines = Path(fin).read_text(encoding=\"utf-8\").splitlines()\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n ds = Dataset.from_dict({\"text\": lines, \"text_id\": list(range(len(lines)))})\r\n tok = partial(tokenize, nlp)\r\n ds = ds.map(tok, load_from_cache_file=True)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\n... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):\r\n\r\n```python\r\nfrom functools import partial\r\n\r\nimport spacy\r\nfrom datasets import load_dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, sample):\r\n return {\"tok\": [t.text for t in nlp(sample[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n tok_func = partial(tokenize, nlp)\r\n ds = load_dataset('text', data_files=fin)\r\n ds = ds[\"train\"].map(tok_func)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```",
"It looks like every time you load `en_core_web_sm` you get a different python object:\r\n```python\r\nimport spacy\r\nfrom datasets.fingerprint import Hasher\r\n\r\nnlp1 = spacy.load(\"en_core_web_sm\")\r\nnlp2 = spacy.load(\"en_core_web_sm\")\r\nHasher.hash(nlp1), Hasher.hash(nlp2)\r\n# ('f6196a33882fea3b', 'a4c676a071f266ff')\r\n```\r\nHere is a list of attributes that have different hashes for `nlp1` and `nlp2`:\r\n- tagger\r\n- parser\r\n- entity\r\n- pipeline (it's the list of the three attributes above)\r\n\r\nI just took a look at the tagger for example and I found subtle differences (there may be other differences though):\r\n```python\r\nnlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id\r\n# (1721, 2243)\r\n```\r\n\r\nWe can try to find all the differences and find the best way to hash those objects properly",
"Thanks for searching! I went looking, and found that this is an implementation detail of thinc\r\n\r\nhttps://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98\r\n\r\nPresumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).\r\n\r\nDo you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher.",
"It can be even simpler to hash the bytes of the pipeline instead\r\n```python\r\nnlp1.to_bytes() == nlp2.to_bytes() # True\r\n```\r\n\r\nIMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).\r\nWhat could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.\r\n\r\nFinally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.\r\n```python\r\n@datasets.register_hash(spacy.language.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n```",
"I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.\r\n\r\nI agree that having the option to use a custom hasher would be useful. I like your suggestion!\r\n\r\nEDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https://spacy.io/usage/saving-loading#pipeline\r\n\r\n> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them – not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.\r\n\r\nBest way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.\r\n\r\n",
"Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.\r\n\r\n```shell\r\ngit clone https://github.com/explosion/spaCy.git\r\ncd spaCy/\r\ngit checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf\r\ncd ..\r\n\r\ngit clone https://github.com/BramVanroy/datasets.git\r\ncd datasets\r\ngit checkout registry\r\npip install -e .\r\npip install ../spaCy\r\nspacy download en_core_web_sm\r\n```\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\nfrom datasets.utils.registry import hashers\r\n\r\n@hashers.register(spacy.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"your/large/file\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n # This is now always the same yay!\r\n print(Hasher.hash(nlp))\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n # But this is not...\r\n print(Hasher.hash(tokenize))\r\n # ... because of this\r\n print(Hasher.hash(nlp.__call__))\r\n ds = ds[\"train\"].map(tokenize)\r\n print(ds[0:2])\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```",
"Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing.",
"I don't quite understand the design constraints of `datasets` or the script that you're running, but my usual advice is to avoid using pickle unless you _absolutely_ have to. So for instance instead of doing your `partial` over the `nlp` object itself, can you just pass the string `en_core_web_sm` in? This will mean calling `spacy.load()` inside the work function, but this is no worse than having to call `pickle.load()` on the contents of the NLP object anyway -- in fact you'll generally find `spacy.load()` faster, apart from the disk read.\r\n\r\nIf you need to pass in the bytes data and don't want to read from disk, you could do something like this:\r\n\r\n```\r\nmsg = (nlp.lang, nlp.to_bytes())\r\n\r\ndef unpack(lang, bytes_data):\r\n return spacy.blank(lang).from_bytes(bytes_data)\r\n```\r\n\r\nI think that should probably work: the Thinc `model.to_dict()` method (which is used by the `model.to_bytes()` method) doesn't pack the model's ID into the message, so the `nlp.to_bytes()` that you get shouldn't be affected by the global IDs. So you should get a clean message from `nlp.to_bytes()` that doesn't depend on the global state.",
"Hi Matthew, thanks for chiming in! We are currently implementing exactly what you suggest: `to_bytes()` as a default before pickling - but we may prefer `to_dict` to avoid double dumping.\r\n\r\n`datasets` uses pickle dumps (actually dill) to get unique representations of processing steps (a \"fingerprint\" or hash). So it never needs to re-load that dump - it just needs its value to create a hash. If a fingerprint is identical to a cached fingerprint, then the result can be retrieved from the on-disk cache. (@lhoestq or @mariosasko can correct me if I'm wrong.)\r\n\r\nI was experiencing the issue that parsing with spaCy gave me a different fingerprint on every run of the script and thus it could never load the processed dataset from cache. At first I thought the reason was that spaCy Language objects were not picklable with recursive dill, but even after [adjusting for that](https://github.com/explosion/spaCy/pull/9593) the issue persisted. @lhoestq found that this is due to the changing `id`, which you discussed [here](https://github.com/explosion/spaCy/discussions/9609#discussioncomment-1661081). So yes, you are right. On the surface there simply seems to be an incompatibility between `datasets` default caching functionality as it is currently implemented and `spacy.Language`.\r\n\r\nThe [linked PR](https://github.com/huggingface/datasets/pull/3224) aims to remedy that, though. Up to now I have put some effort into making it easier to define your own \"pickling\" function for a given type (and optionally any of its subclasses). That allows us to tell `datasets` that instead of doing `dill.save(nlp)` (non-deterministic), to use `dill.save(nlp.to_bytes())` (deterministic). When I find some more time, the PR [will be expanded](https://github.com/huggingface/datasets/pull/3224#issuecomment-968958528) to improve the user-experience a bit and add a built-in function to pickle `spacy.Language` as one of the defaults (using `to_bytes()`).",
"Is there a workaround for this? maybe by explicitly requesting datasets to cache the result of `.map()`?",
"Hi ! If your function is not picklable, then the fingerprint of the resulting dataset can't be computed. The fingerprint is a hash that is used by the cache to reload previously computed datasets: the dataset file is named `cache-<fingerprint>.arrow` in your dataset's cache directory.\r\n\r\nAs a workaround you can set the fingerprint that is going to be used by the cache:\r\n```python\r\nresult = my_dataset.map(func, new_fingerprint=new_fingerprint)\r\n```\r\nAny future call to `map` with the same `new_fingerprint` will reload the result from the cache.\r\n\r\n**Be careful using this though: if you change your `func`, be sure to change the `new_fingerprint` as well.**",
"I've been having an issue that might be related to this when trying to pre-tokenize a corpus and caching it for using it later in the pre-training of a RoBERTa model. I always get the following warning:\r\n\r\n```\r\nDataset text downloaded and prepared to /gpfswork/rech/project/user/.cache/hf-datasets/text/default-1850886023af0077/0.0.0/acc32f2f2ef863c93c2f30c52f7df6cc9053a1c2230b8d7da0d210404683ca08. Subsequent calls will reuse this data.\r\nParameter 'function'=<function encode_dataset.<locals>.<lambda> at 0x14a92157b280> of the transform datasets.arrow_dataset.Dataset.filter@2.0.1 couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n```\r\n\r\nAnd when I launch the pre-training the pre-tokenized corpus is not found and it is tokenized again, which makes me waste precious GPU hours.\r\n\r\nFor me, the workaround was downgrading `dill` and `multiprocess` to the following versions:\r\n\r\n```\r\ndill 0.3.4\r\nmultiprocess 0.70.12.2 \r\n```",
"> Hi ! If your function is not picklable, then the fingerprint of the resulting dataset can't be computed. The fingerprint is a hash that is used by the cache to reload previously computed datasets: the dataset file is named `cache-<fingerprint>.arrow` in your dataset's cache directory.\r\n> \r\n> As a workaround you can set the fingerprint that is going to be used by the cache:\r\n> \r\n> ```python\r\n> result = my_dataset.map(func, new_fingerprint=new_fingerprint)\r\n> ```\r\n> \r\n> Any future call to `map` with the same `new_fingerprint` will reload the result from the cache.\r\n> \r\n> **Be careful using this though: if you change your `func`, be sure to change the `new_fingerprint` as well.**\r\n\r\nIs the argument `new_fingerprint` available for datasetDict ? I can only use it on arrow datasets but might be useful to generalize it to DatasetDict's map as well ? @lhoestq ",
"> I've been having an issue that might be related to this when trying to pre-tokenize a corpus and caching it for using it later in the pre-training of a RoBERTa model. I always get the following warning:\r\n> \r\n> ```\r\n> Dataset text downloaded and prepared to /gpfswork/rech/project/user/.cache/hf-datasets/text/default-1850886023af0077/0.0.0/acc32f2f2ef863c93c2f30c52f7df6cc9053a1c2230b8d7da0d210404683ca08. Subsequent calls will reuse this data.\r\n> Parameter 'function'=<function encode_dataset.<locals>.<lambda> at 0x14a92157b280> of the transform datasets.arrow_dataset.Dataset.filter@2.0.1 couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n> ```\r\n> \r\n> And when I launch the pre-training the pre-tokenized corpus is not found and it is tokenized again, which makes me waste precious GPU hours.\r\n> \r\n> For me, the workaround was downgrading `dill` and `multiprocess` to the following versions:\r\n> \r\n> ```\r\n> dill 0.3.4\r\n> multiprocess 0.70.12.2 \r\n> ```\r\n\r\nThis worked for me - thanks!",
"I see this has just been closed - it seems quite relevant to another tokenizer I have been trying to use, the `vinai/phobert` family of tokenizers\r\n\r\nhttps://huggingface.co/vinai/phobert-base\r\nhttps://huggingface.co/vinai/phobert-large\r\n\r\nI ran into an issue where a large dataset took several hours to tokenize, the process hung, and I was unable to use the cached version of the tokenized data:\r\n\r\nhttps://discuss.huggingface.co/t/cache-parallelize-long-tokenization-step/25791/3\r\n\r\nI don't see any way to specify the hash of the tokenizer or the fingerprint of the tokenized data to use, so is the tokenized dataset basically lost at this point? Is there a good way to avoid this happening again if I retokenize the data?\r\n",
"In your case it looks like the job failed before caching the data - maybe one of the processes crashed",
"Interesting. Thanks for the observation. Any suggestions on how to start tracking that down? Perhaps run it singlethreaded and see if it crashes?",
"You can monitor your RAM and disk space in case a process dies from OOM or disk full, and when it hangs you can check how many processes are running. IIRC there are other start methods for multiprocessing in python that may show an error message if a process dies.\r\n\r\nRunning on a single process can also help debugging this indeed",
"https://github.com/huggingface/datasets/issues/3178#issuecomment-1189435462\r\n\r\nThe solution does not solve for using commonvoice dataset (\"mozilla-foundation/common_voice_11_0\")",
"Hi @tung-msol could you open a new issue and share the error you got and the map function you used ?",
"I faced the same problem even after using these versions for python 3.10:\r\n\r\n> dill 0.3.4\r\n> multiprocess 0.70.12.2 \r\n\r\nHowever, doing `result = my_dataset.map(func, new_fingerprint=new_fingerprint)` worked!",
"> dataset.map(func, new_fingerprint=new_fingerprint)\r\n\r\ndid you mean `new_fingerprint=\"new_fingerprint\"`, or did you define before this call?",
"You should define your fingerprint as a string that identifies your dataset. By default it's a hash computed from the data files and on the applied transformations."
] |
2021-10-29T12:56:09Z
|
2024-08-19T13:03:49Z
|
2022-11-02T17:18:43Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
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I_kwDODunzps499VMk
| 3,177
|
More control over TQDM when using map/filter with multiple processes
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"Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n def wrapper(*args, **kwargs):\r\n rank = kwargs.get(\"rank\")\r\n disable_tqdm = kwargs.get(\"disable_tqdm\", False)\r\n disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm\r\n kwargs[\"disable_tqdm\"] = disable_tqdm\r\n return func(*args, **kwargs)\r\n return wrapper\r\n \r\ndatasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)\r\n``` \r\n\r\nEDIT: Ups, closed by accident.\r\n\r\nThanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.\r\n\r\nDo you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.\r\n\r\n",
"Inspiration may be found at `transformers`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a/src/transformers/trainer.py#L1231-L1233\r\n\r\nTo get unique IDs for each worker, see https://stackoverflow.com/a/10192611/1150683"
] |
2021-10-29T11:56:16Z
|
2023-02-13T20:16:40Z
|
2023-02-13T20:16:40Z
|
CONTRIBUTOR
| null | null | null | null |
It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.
```python
dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6)
```
The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.
```
#0: 0%| | 0/13 [00:00<?, ?ba/s]
#1: 0%| | 0/13 [00:00<?, ?ba/s]
#2: 0%| | 0/13 [00:00<?, ?ba/s]
#3: 0%| | 0/13 [00:00<?, ?ba/s]
#4: 0%| | 0/13 [00:00<?, ?ba/s]
#5: 0%| | 0/13 [00:00<?, ?ba/s]
#0: 8%| | 1/13 [00:00<?, ?ba/s]
#1: 8%| | 1/13 [00:00<?, ?ba/s]
...
```
Instead, it would be welcome if we had the option to only show the progress of rank 0.
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`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`
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[
"NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.",
"Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.",
"Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`. Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n def __call__() -> DatasetDict:\r\n # load and preprocess the data\r\n return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n def prepare_data():\r\n self.builder()\r\n def setup():\r\n self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project structure was:\r\n\r\n```\r\nsrc/\r\n | - cli.py\r\n | - training/\r\n | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n # preprocess data and run\r\n \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!",
"Please allow me to revive this discussion, as I have an extremely similar issue. Instead of an error, my datasets functions simply aren't caching properly. My setup is almost the same as yours, with hydra to configure my experiment parameters.\r\n\r\n@vlievin Could you confirm if your code correctly loads the cache? If so, do you have any public code that I can reference for comparison?\r\n\r\nI will post a full example with hydra that illustrates this problem in a little bit, probably on another thread.",
"Hello @mariomeissner, very sorry for the late reply, I hope you have found a solution to your problem!\r\n\r\nI don't have public code at the moment. I have not experienced any other issue with hydra, even if I don't understand why changing the location of the definition of `run()` fixed the problem. \r\n\r\nOverall, I don't have issue with caching anymore, even when \r\n1. using custom fingerprints using the argument `new_fingerprint \r\n2. when using `num_proc>1`",
"I solved my issue by turning the map callable into a class static method, like they do in `lightning-transformers`. Very strange...",
"I have this issue with datasets v2.5.2 with Python 3.8.10 on Ubuntu 20.04.4 LTS. It does not occur when num_proc=1. When num_proc>1, it intermittently occurs and will cause process to hang. As previously mentioned, it occurs even when datasets have been previously cached. I have tried wrapping logic in a static class as suggested with @mariomeissner with no improvement.",
"@philipchung hello ,i have the same issue like yours,did you solve it?",
"No. I was not able to get num_proc>1 to work.",
"same problem here. It randomly occurs...",
"Can someone provide a reproducer to help us debug this (e.g., a `hydra` repo with dummy model and data)?",
"Hi, similarly here, is there any update for this issue? Particularly having the exact same message as in #6393, not sure this is a datasets or llm-foundry issue"
] |
2021-10-28T10:29:00Z
|
2024-04-02T18:13:21Z
|
2021-11-03T11:26:10Z
|
NONE
| null | null | null | null |
## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
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| 1,037,728,059
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I_kwDODunzps492nk7
| 3,171
|
Raise exceptions instead of using assertions for control flow
|
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[
"Adding the remaining tasks for this issue to help new code contributors. \r\n$ cd src/datasets && ack assert -lc \r\n- [x] commands/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils/py_utils.py:2\r\n- [x] features/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table.py:1\r\n- [x] metric.py:3\r\n- [x] tasks/image_classification.py:1\r\n- [x] arrow_dataset.py:17\r\n- [x] fingerprint.py:6\r\n- [x] io/json.py:1\r\n- [x] io/csv.py:1",
"Hi all,\r\nI am interested in taking up `fingerprint.py`, `search.py`, `arrow_writer.py` and `metric.py`. Will raise a PR soon!",
"Let me look into `arrow_dataset.py`, `table.py`, `data_files.py` & `features.py` ",
"All the tasks are completed for this issue. This can be closed. "
] |
2021-10-27T18:26:52Z
|
2021-12-23T16:40:37Z
|
2021-12-23T16:40:37Z
|
COLLABORATOR
| null | null | null | null |
Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.
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| 1,036,673,263
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OpenSLR/83 is empty
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[
"Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?",
"@albertvillanova Yes. Figured I introduced the broken config, I should fix it too.\r\n\r\nI've got it working, but I'm struggling with one of the tests. I've started a PR so I/we can work through it.",
"Looks like the tests all passed on the PR."
] |
2021-10-26T19:42:21Z
|
2021-10-29T10:04:09Z
|
2021-10-29T10:04:09Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
As the summary says, openslr / SLR83 / train is empty.
The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('openslr', 'SLR83')
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 17877
})
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 0
})
})
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.1.dev0 (master HEAD)
- Platform: Ubuntu 20.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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[
"Hi ! Thanks for reporting :) I think #3280 should fix this",
"I tried with the latest changes from #3280 on google colab and it worked fine :)\r\nWe'll do a new release soon, in the meantime you can use the updated version with:\r\n```python\r\nload_dataset(\"bookcorpusopen\", revision=\"master\")\r\n```",
"Fixed by #3280."
] |
2021-10-26T16:06:15Z
|
2021-11-17T15:53:46Z
|
2021-11-17T15:53:46Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).
This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.
## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
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I_kwDODunzps49xvTm
| 3,165
|
Deprecate prepare_module
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2021-10-26T15:27:15Z
|
2021-11-05T09:27:36Z
|
2021-11-05T09:27:36Z
|
MEMBER
| null | null | null | null |
In version 1.13, `prepare_module` was deprecated.
Add deprecation warning and remove its usage from all the library.
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I_kwDODunzps49uvXu
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Add raw data files to the Hub with GitHub LFS for canonical dataset
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[
"Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.\r\n\r\nThe only difference with a \"canonical\"/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance – completely up to you) but then you can upload your data using git-lfs (unlike \"canonical\" datasets where we don't host the data)\r\n\r\nLet me know if this fits your use case!\r\n\r\ncc'ing @osanseviero @lhoestq and rest of the team 🤗",
"Hi @zlucia,\r\n\r\nAs @julien-c pointed out, the way to store/host raw data files in our Hub is by using what we call \"community\" datasets:\r\n- either at your personal namespace: `load_dataset(\"zlucia/casehold\")`\r\n- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset(\"reglab/casehold\")`\r\n\r\nPlease note that \"canonical\" datasets do not normally store/host their raw data at our Hub, but in a third-party server. For \"canonical\" datasets, we just host the \"loading script\", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.\r\n\r\nIn order to create an organization namespace in our Hub, please follow this link: https://huggingface.co/organizations/new\r\n\r\nThere are already many organizations at our Hub (complete list here: https://huggingface.co/organizations), such as:\r\n- Stanford CRFM: https://huggingface.co/stanford-crfm\r\n- Stanford NLP: https://huggingface.co/stanfordnlp\r\n- Stanford CS329S: Machine Learning Systems Design: https://huggingface.co/stanford-cs329s\r\n\r\nAlso note that you in your organization namespace:\r\n- you can add any number of members\r\n- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`\r\n\r\nOnce you have created an organization, these are the steps to upload/host a raw dataset: \r\n- The no-code procedure: https://huggingface.co/docs/datasets/upload_dataset.html\r\n- Using the command line (terminal): https://huggingface.co/docs/datasets/share.html#add-a-community-dataset\r\n\r\nPlease, feel free to ping me if you have any further questions or need help.\r\n",
"Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal namespace."
] |
2021-10-25T23:28:21Z
|
2021-10-30T19:54:51Z
|
2021-10-30T19:54:51Z
|
NONE
| null | null | null | null |
I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!
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`datasets-cli test` should work with datasets without scripts
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[
"> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).\r\n> \r\n> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!\r\n\r\nwhy don't you try to share that info with people, so you can also save some days.",
"Hi ! You can run the command if you download the repository\r\n```\r\ngit clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n```\r\nand run the command\r\n```\r\ndatasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n```\r\n\r\n(though on my side it doesn't manage to download the data since the dataset is private ^^)",
"> Hi ! You can run the command if you download the repository\r\n> \r\n> ```\r\n> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n> ```\r\n> \r\n> and run the command\r\n> \r\n> ```\r\n> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n> ```\r\n> \r\n> (though on my side it doesn't manage to download the data since the dataset is private ^^)\r\n\r\nHi! Thanks for the info. \r\ngit cannot find the repository. Do you know if they have depreciated these tests and created a new one?",
"I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`",
"> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`\r\n\r\nyour example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot"
] |
2021-10-25T18:52:30Z
|
2021-11-25T16:04:29Z
| null |
CONTRIBUTOR
| null | null | null | null |
It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
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I_kwDODunzps49qL2V
| 3,155
|
Illegal instruction (core dumped) at datasets import
|
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[
"It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors."
] |
2021-10-24T17:21:36Z
|
2021-11-18T19:07:04Z
|
2021-11-18T19:07:03Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"
## Steps to reproduce the bug
```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```
## Environment info
When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"
If I run the following commands:
```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```
Then I get:
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
Let me know what additional information you need in order to debug this issue. Thanks in advance!
|
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| 1,034,361,806
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I_kwDODunzps49pxvO
| 3,154
|
Sacrebleu unexpected behaviour/requirement for data format
|
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[] | null |
[
"Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n] # len(refs) = 2\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nInstead, it should be:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'The dog had bit the man.'],\r\n ['It was not unexpected.', 'No one was surprised.'],\r\n ['The man bit him first.', 'The man had bitten the dog.'], \r\n] # len(refs) = 3\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nHowever, `sacreblue` works with the format that's described in your example, hence this part:\r\nhttps://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99\r\n\r\nHope you get an idea!",
"Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library/metric. But it is understandable due to how `datasets` works!"
] |
2021-10-24T08:55:33Z
|
2021-10-31T09:08:32Z
|
2021-10-31T09:08:31Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
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I_kwDODunzps49nwRq
| 3,150
|
Faiss _is_ available on Windows
|
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| false
| null |
[] | null |
[
"Sure, feel free to open a PR."
] |
2021-10-22T18:07:16Z
|
2021-11-02T10:06:03Z
|
2021-11-02T10:06:03Z
|
CONTRIBUTOR
| null | null | null | null |
In the setup file, I find the following:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171
However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.
(This isn't really a bug but didn't know how else to tag.)
If you agree I can do a quick PR and remove that line.
|
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I_kwDODunzps49nMjY
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|
Streaming with num_workers != 0
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[] | null |
[
"I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. ",
"Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.\r\n\r\n\r\nPytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/datapipes/iter/grouping.py#L13)\r\n",
"Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !",
"fixed by #4375 "
] |
2021-10-22T15:07:17Z
|
2022-07-04T12:14:58Z
|
2022-07-04T12:14:58Z
|
NONE
| null | null | null | null |
## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
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| 1,033,605,947
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I_kwDODunzps49m5M7
| 3,146
|
CLI test command throws NonMatchingSplitsSizesError when saving infos
|
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2021-10-22T13:50:53Z
|
2021-10-27T08:01:49Z
|
2021-10-27T08:01:49Z
|
MEMBER
| null | null | null | null |
When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
service.run()
File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
builder.download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```
This is due because a previous run generated a wrong `dataset_info.json`.
This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`.
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| 1,033,580,009
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I_kwDODunzps49my3p
| 3,145
|
[when Image type will exist] provide a way to get the data as binary + filename
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[
"@severo, maybe somehow related to this PR ?\r\n- #3129",
"@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage). ",
"Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all",
"Fixed with https://github.com/huggingface/datasets/pull/3163"
] |
2021-10-22T13:23:49Z
|
2021-12-22T11:05:37Z
|
2021-12-22T11:05:36Z
|
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
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I_kwDODunzps49mxWA
| 3,144
|
Infer the features if missing
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"Done by @lhoestq here: https://github.com/huggingface/datasets/pull/4500 (https://github.com/huggingface/datasets/pull/4500/files#diff-02930e1d966f4b41f9ddf15d961f16f5466d9bee583138657018c7329f71aa43R1255 in particular)\r\n"
] |
2021-10-22T13:17:33Z
|
2022-09-08T08:23:10Z
|
2022-09-08T08:23:10Z
|
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Some datasets, in particular community datasets, have no info file, thus no features.
**Describe the solution you'd like**
If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.
Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.
**Describe alternatives you've considered**
The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal.
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I_kwDODunzps49mwV3
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|
Provide a way to check if the features (in info) match with the data of a split
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[
"Related: #3144 "
] |
2021-10-22T13:13:36Z
|
2021-10-22T13:17:56Z
| null |
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
I understand that currently the data loaded has not always the type described in the info features
**Describe the solution you'd like**
Provide a way to check if the rows have the type described by info features
**Describe alternatives you've considered**
Always check it, and raise an error when loading the data if their type doesn't match the features.
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I_kwDODunzps49mvdS
| 3,142
|
Provide a way to write a streamed dataset to the disk
|
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[
"Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset.",
"@mariosasko Hi big brother,any update on this? It's 2024 with large streamed dataset loading consume too much time(exp. 2day..), really need this feature for what TF does"
] |
2021-10-22T13:09:53Z
|
2024-01-12T07:26:43Z
| null |
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
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I_kwDODunzps49mlNv
| 3,139
|
Fix file/directory deletion on Windows
|
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2021-10-22T12:22:08Z
|
2021-10-22T12:22:08Z
| null |
COLLABORATOR
| null | null | null | null |
Currently, on Windows, some attempts to delete a dataset file/directory will fail with the `PerimissionError`.
Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.
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I_kwDODunzps49mCCd
| 3,138
|
More fine-grained taxonomy of error types
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2021-10-22T09:35:29Z
|
2022-09-20T13:04:42Z
| null |
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did
**Describe the solution you'd like**
Give a specific exception type for every group of similar errors
**Describe alternatives you've considered**
Rely on the error message, using regex
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I_kwDODunzps49ltHb
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|
Make inspect.get_dataset_config_names always return a non-empty list of configs
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[
"Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?",
"Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)"
] |
2021-10-22T08:02:50Z
|
2021-10-28T05:44:49Z
|
2021-10-28T05:44:49Z
|
COLLABORATOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
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I_kwDODunzps49liur
| 3,134
|
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
|
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[
"Hi,\r\n\r\nDid you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py`, so this code is working without an error on my side. \r\n\r\nAdditionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?",
"Same issue when running `metric = datasets.load_metric(\"accuracy\")`.\r\nError info is:\r\n```\r\nmetric = datasets.load_metric(\"accuracy\")\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-2-d25db38b26c5>\", line 1, in <module>\r\n metric = datasets.load_metric(\"accuracy\")\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 610, in load_metric\r\n module_path, _ = prepare_module(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 330, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 288, in cached_path\r\n output_path = get_from_cache(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 605, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py\r\n```\r\n\r\n\r\n My `datasets-cli env` result is as follows:\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n@yananchen1989 did you find a way to solve this?",
"It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. \r\nchange `metric = datasets.load_metric(\"accuracy\")` to `metric = datasets.load_metric(path = \"./accuracy.py\")`.\r\nCopy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)",
"> It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. change `metric = datasets.load_metric(\"accuracy\")` to `metric = datasets.load_metric(path = \"./accuracy.py\")`. Copy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)\r\n\r\nThis is really a good way"
] |
2021-10-22T07:07:52Z
|
2023-09-14T01:19:45Z
|
2022-01-19T14:02:31Z
|
NONE
| null | null | null | null |
datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ?
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I_kwDODunzps49ishW
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Support Audio feature in streaming mode
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2021-10-21T13:32:18Z
|
2021-11-12T14:13:04Z
|
2021-11-12T14:13:04Z
|
MEMBER
| null | null | null | null |
Currently, Audio feature is only supported for non-streaming datasets.
Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.
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I_kwDODunzps49h8xp
| 3,131
|
Add ADE20k
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"I think we can close this issue since PR [#3607](https://github.com/huggingface/datasets/pull/3607) solves this."
] |
2021-10-21T10:13:09Z
|
2023-01-27T14:40:20Z
|
2023-01-27T14:40:20Z
|
CONTRIBUTOR
| null | null | null | null |
## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Support Audio feature for TAR archives in sequential access
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2021-10-21T08:23:01Z
|
2021-11-17T17:42:07Z
|
2021-11-17T17:42:07Z
|
MEMBER
| null | null | null | null |
Currently, Audio feature accesses each audio file by their file path.
However, streamed TAR archive files do not allow random access to their archived files.
Therefore, we should enhance the Audio feature to support TAR archived files in sequential access.
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I_kwDODunzps49hJsF
| 3,127
|
datasets-cli: convertion of a tfds dataset to a huggingface one.
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| null |
[] | null |
[
"Hi,\r\n\r\nthe MNIST dataset is already available on the Hub. You can use it as follows:\r\n```python\r\nimport datasets\r\ndataset_dict = datasets.load_dataset(\"mnist\")\r\n```\r\n\r\nAs for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned."
] |
2021-10-21T06:14:27Z
|
2021-10-27T11:36:05Z
| null |
NONE
| null | null | null | null |
### Discussed in https://github.com/huggingface/datasets/discussions/3079
<div type='discussions-op-text'>
<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.
I've tried:
1. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/
2. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/
and other permutations.
The script appears to be running and finishing without an error but when looking in the huggingface/datasets/ folder nothing is created.
</div>
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| 1,032,093,055
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I_kwDODunzps49hH1_
| 3,126
|
"arabic_billion_words" dataset does not create the full dataset
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[
"Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it."
] |
2021-10-21T06:02:38Z
|
2021-10-22T13:28:40Z
|
2021-10-22T13:28:40Z
|
NONE
| null | null | null | null |
## Describe the bug
When running:
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB)
## Expected results
over 100K sentences
## Actual results
only 11K sentences
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
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I_kwDODunzps49f-o3
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Segmentation fault when loading datasets from file
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[
"Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though",
"The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists"
] |
2021-10-20T20:16:11Z
|
2021-11-02T14:57:07Z
|
2021-11-02T14:57:07Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/
## Steps to reproduce the bug
Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```
## Expected results
a `tiny_kelm` functional dataset
## Actual results
☠️ `Segmentation fault (core dumped)` ☠️
## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
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I_kwDODunzps49f9P1
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OSError with a custom dataset loading script
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[
"Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla/janes_tag` and the `classla/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https://huggingface.co/datasets/classla/janes_tag/blob/main/janes_tag.py#L86\r\n\r\nLet me know if this works for you.",
"Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla/janes_tag` zip file so that it also contains the `data` directory, but I am still getting the same error.",
"Hi,\r\n\r\nI just tried to download the `classla/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.",
"I am still getting the same error.",
"Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla/janes_tag', split='validation', cache_dir=\"path/to/different/cache/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~/.cache/huggingface/datasets/downloads/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```",
"Thank you, deleting the `~/.cache/huggingface/datasets/downloads/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`",
"Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script/data on the Hub.",
"It works now, thank you!"
] |
2021-10-20T20:08:39Z
|
2021-11-23T09:55:38Z
|
2021-11-23T09:55:38Z
|
NONE
| null | null | null | null |
## Describe the bug
I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('classla/janes_tag', split='validation')
```
## Expected results
Dataset correctly loaded.
## Actual results
Traceback (most recent call last):
File "C:/mypath/test.py", line 91, in <module>
load_and_print('janes_tag')
File "C:/mypath/test.py", line 32, in load_and_print
dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation')
File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.5
- PyArrow version: 3.0.0
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I_kwDODunzps49eNEs
| 3,119
|
Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech
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[
"Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files."
] |
2021-10-20T12:05:07Z
|
2021-10-22T19:00:52Z
|
2021-10-22T08:30:22Z
|
CONTRIBUTOR
| null | null | null | null |
## Adding a Dataset
- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
The *openslr* dataset already exists, this will add additional subset, *SLR83*.
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CI error at each release commit
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2021-10-20T11:42:53Z
|
2021-10-20T13:02:35Z
|
2021-10-20T13:02:35Z
|
MEMBER
| null | null | null | null |
After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110
```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>
def test_load_dataset_canonical(self):
scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
with self.assertRaises(FileNotFoundError) as context:
datasets.load_dataset("_dummy")
self.assertIn(
f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
> str(context.exception),
)
E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
tests\test_load.py:358: AssertionError
```
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I_kwDODunzps49byEK
| 3,114
|
load_from_disk in DatasetsDict/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem
|
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[
"Hi ! Can you try again with pyarrow 6.0.0 ? I think it includes some changes regarding filesystems compatibility with fsspec.",
"Hi @lhoestq! I ended up using `fsspec.implementations.arrow.HadoopFileSystem` which doesn't have the problem I described with pyarrow 5.0.0.\r\n\r\nI'll try again with `PyArrowHDFS` once I update arrow to 6.0.0.\r\n\r\nThanks!"
] |
2021-10-19T20:01:45Z
|
2022-02-14T14:00:28Z
|
2022-02-14T14:00:28Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.
## Steps to reproduce the bug
The documentation for the `fs` parameter states:
```
fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):
Instance of the remote filesystem used to download the files from.
```
`PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.
```python
from fsspec.implementations.hdfs import PyArrowHDFS
...
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
```
## Expected results
Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:
```python
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
my_datasets.save_to_disk(transformed_corpus_path, fs=fs)
```
As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:
```sh
$ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/"
Found 4 items
-rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation
```
I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above.
## Actual results
However, when trying to recover the saved datasets, I get this error:
```
...
File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download
TypeError: download() got an unexpected keyword argument 'recursive'
```
Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter:
```python
def download(self, path, stream, buffer_size=None):
with self.open(path, 'rb') as f:
f.download(stream, buffer_size=buffer_size)
```
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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I_kwDODunzps49br0b
| 3,113
|
Loading Data from HDF files
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[] | null |
[
"I'm currently working on bringing [Ecoset](https://www.pnas.org/doi/10.1073/pnas.2011417118) to huggingface datasets and I would second this request...",
"I would also like this support or something similar. Geospatial datasets come in netcdf which is derived from hdf5, or zarr. I've gotten zarr stores to work with datasets and streaming, but it takes awhile to convert the data to zarr if it's not stored in that natively. ",
"@mariosasko , I would like to contribute on this \"good second issue\" . Is there anything in the works for this Issue or can I go ahead ? \r\n",
"Hi @VijayKalmath! As far as I know, nobody is working on it, so feel free to take over. Also, before you start, I suggest you comment `#self-assign` on this issue to assign it to yourself.",
"#self-assign",
"Hey @mariosasko can you assign this issue to me !!",
"So basically, we just need to load HDF5 files to Parquet?\r\n\r\ne.g. Like this? https://stackoverflow.com/questions/46157709/converting-hdf5-to-parquet-without-loading-into-memory",
"#self-assign\nHi! I'd love to take a shot at implementing this feature to allow loading HDF5 datasets via Hugging Face Datasets. Will keep you updated here!",
"Hey @mariosasko,\nI've opened a PR to address this: #7625\nIt adds a new h5folder loader to support .h5 files via load_dataset(\"h5folder\", data_dir=...).\nFeedback welcome!"
] |
2021-10-19T19:26:46Z
|
2025-08-19T13:28:54Z
|
2025-08-19T13:28:54Z
|
NONE
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.
**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).
**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.
**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.
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OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
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[
"I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.",
"fixed",
"Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:",
"Actually this is is a live bug, documented yet still live so reopening"
] |
2021-10-19T18:21:41Z
|
2021-10-19T18:52:29Z
| null |
NONE
| null | null | null | null |
## Describe the bug
Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :
> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
Note that I always run `batch_size=writer_batch_size` :
## Steps to reproduce the bug
```python
datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla")
```
## Introspecting CUDA memory during bug
Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb
`print(torch.cuda.memory_summary(device=device, abbreviated=False))`
> |===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B |
| from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B |
| from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B |
|---------------------------------------------------------------------------|
| Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB |
| from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB |
| from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB |
|---------------------------------------------------------------------------|
| Allocations | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| Active allocs | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| GPU reserved segments | 21 | 21 | 21 | 0 |
| from large pool | 20 | 20 | 20 | 0 |
| from small pool | 1 | 1 | 1 | 0 |
|---------------------------------------------------------------------------|
| Non-releasable allocs | 18 | 23 | 166 | 148 |
| from large pool | 17 | 18 | 19 | 2 |
| from small pool | 1 | 6 | 147 | 146 |
|===========================================================================|
## Expected results
Efficiently process the datasets and write it down to disk.
## Actual results
--------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2390 else:
-> 2391 writer.write(example)
2392 else:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size)
367
--> 368 self.write_examples_on_file()
369
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module>
3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon')
4 #tracker.start()
----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,
6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,
7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #
~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)
481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):
482 if "xxx_" in column:
--> 483 dataset = dataset.map(lambda example :
484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column],
485 token_type_ids=example[column.replace("input_ids","token_type_ids")],
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2034
2035 if num_proc is None or num_proc == 1:
-> 2036 return self._map_single(
2037 function=function,
2038 with_indices=with_indices,
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2425 if update_data:
2426 if writer is not None:
-> 2427 writer.finalize()
2428 if tmp_file is not None:
2429 tmp_file.close()
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream)
440 # Re-intializing to empty list for next batch
441 self.hkey_record = []
--> 442 self.write_examples_on_file()
443 if self.pa_writer is None:
444 if self._schema is not None:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
319 type(pa_array)
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
##Next steps
Testing on Linux.
@albertvillanova
|
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| 1,030,598,983
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I_kwDODunzps49bbFH
| 3,111
|
concatenate_datasets removes ClassLabel typing.
|
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[
"Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1"
] |
2021-10-19T18:05:31Z
|
2021-10-21T14:50:21Z
|
2021-10-21T14:50:21Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When concatenating two datasets, we lose typing of ClassLabel columns.
I can work on this if this is a legitimate bug,
## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets
DS_LEN = 100
my_dataset = Dataset.from_dict(
{
"sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
"label": [i % 2 for i in range(DS_LEN)]
}
)
my_predictions = Dataset.from_dict(
{
"pred": [(i + 1) % 2 for i in range(DS_LEN)]
}
)
my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)
concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)
```
## Expected results
The features of `concat_ds` should contain ClassLabel.
## Actual results
On master, I get:
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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I_kwDODunzps49Vs1b
| 3,105
|
download_mode=`force_redownload` does not work on removed datasets
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[] |
2021-10-18T13:12:38Z
|
2021-10-22T09:36:10Z
| null |
COLLABORATOR
| null | null | null | null |
## Describe the bug
If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead.
## Steps to reproduce the bug
_requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981
```python
import datasets as ds
dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload')
```
## Expected results
It should raise an exception, since the dataset does not exist anymore.
## Actual results
It uses the cached result
```
Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub.
```
## Environment info
- `datasets` version: 1.13.4.dev0
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
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Missing Zenodo 1.13.3 release
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"Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150"
] |
2021-10-18T12:57:18Z
|
2021-10-22T13:22:25Z
|
2021-10-22T13:22:24Z
|
MEMBER
| null | null | null | null |
After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305
TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed
|
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Unsuitable project description in PyPI
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2021-10-18T12:45:00Z
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2021-10-18T12:59:56Z
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2021-10-18T12:59:56Z
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MEMBER
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Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/
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[
"Hi @JTWang2000, thanks for reporting.\r\n\r\nHowever, I cannot reproduce your reported bug:\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"sst\", \"default\")\r\n>>> dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 8544\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 1101\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 2210\r\n })\r\n})\r\n```\r\n\r\nMaybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n```\r\npip install -U huggingface_hub\r\n```",
"Im facing the same issue. I did run the upgrade command but that doesnt seem to resolve the issue",
"Hi @aneeshjain, could you please specify which `huggingface_hub` version you are using?\r\n\r\nBesides that, please run `datasets-cli env` and copy-and-paste its output below.",
"The problem seems to be with the latest version of `datasets`. After running `pip install -U datasets huggingface_hub`, I get the following: \r\n\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/data_files.py\", line 122, in <module>\r\n allowed_extensions: Optional[list] = None,\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n````\r\nNote that pip reports the latest `datasets` version as \r\n```bash\r\n pip show datasets\r\nName: datasets\r\nVersion: 1.14.0\r\n```\r\nHowever, if I downgrade datasets with `pip install datasets==1.11.0`, things now work\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\ndvers=1.11.0\r\n````",
"> Hi @JTWang2000, thanks for reporting.\r\n> \r\n> However, I cannot reproduce your reported bug:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> \r\n> >>> dataset = load_dataset(\"sst\", \"default\")\r\n> >>> dataset\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 8544\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 1101\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 2210\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Maybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n> \r\n> ```\r\n> pip install -U huggingface_hub\r\n> ```\r\n\r\nMy problem solved after updating huggingface hub command. Thanks a lot and sorry for the late reply. ",
"@tjruwase, please note that versions of `datsets` and `huggingface_hub` must be compatible one with each other:\r\n- In `datasets` version `1.11.0`, the requirement on `huggingface_hub` was: `huggingface_hub<0.1.0`\r\n https://github.com/huggingface/datasets/blob/2cc00f372a96133e701275eec4d6b26d15257289/setup.py#L90\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was compatible\r\n- In `datasets` version `1.12.0`, the requirement on `huggingface_hub` was: `huggingface_hub>=0.0.14,<0.1.0`\r\n https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/setup.py#L104\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was no longer compatible \r\n- Currently, in `datasets` version `1.15.1`, the requirement on `huggingface_hub` is: `huggingface_hub>=0.1.0,<1.0.0`\r\n https://github.com/huggingface/datasets/blob/018100679d21cf27136f0eccb1c50e3a9c968ce2/setup.py#L102\r\n\r\n@JTWang2000, thanks for your answer. I close this issue then."
] |
2021-10-17T14:17:47Z
|
2021-11-09T16:42:29Z
|
2021-11-09T16:42:28Z
|
NONE
| null | null | null | null |
## Describe the bug
When using `pip install datasets`
or use `conda install -c huggingface -c conda-forge datasets`
cannot install datasets
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sst", "default")
```
## Actual results
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-fbe7981e6e21> in <module>
1 import torch
2 import transformers
----> 3 from datasets import load_dataset
4
5 dataset = load_dataset("sst", "default")
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module>
35 from .arrow_reader import ArrowReader, ReadInstruction
36 from .arrow_writer import ArrowWriter
---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
38 from .combine import interleave_datasets
39 from .dataset_dict import DatasetDict, IterableDatasetDict
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module>
42 )
43 from .arrow_writer import ArrowWriter, BeamWriter
---> 44 from .data_files import DataFilesDict, _sanitize_patterns
45 from .dataset_dict import DatasetDict, IterableDatasetDict
46 from .fingerprint import Hasher
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module>
118
119 def _exec_patterns_in_dataset_repository(
--> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo,
121 patterns: List[str],
122 allowed_extensions: Optional[list] = None,
AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-11.3.1-arm64-arm-64bit
- Python version: 3.8.10
- PyArrow version: 5.0.0
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[
"Thanks for reporting, @VictorSanh.\r\n\r\nI'm fixing it."
] |
2021-10-15T19:34:38Z
|
2021-10-18T07:51:54Z
|
2021-10-18T07:51:54Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I keep runnig into a fsspec ModuleNotFound error
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_infos
2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module>
from .utils.streaming_download_manager import StreamingDownloadManager
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module>
from fsspec.exceptions import FSTimeoutError
ModuleNotFoundError: No module named 'fsspec.exceptions'
```
Yet, I do have `fsspec`:
```bash
hf@victor-scale:~/dev/promptsource$ pip show fsspec
Name: fsspec
Version: 2021.5.0
Summary: File-system specification
Home-page: http://github.com/intake/filesystem_spec
Author: None
Author-email: None
License: BSD
Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages
Requires:
Required-by: datasets
```
With the same version of fsspec and `datasets==1.9.0`, I don't see this problem....
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I can't even run `datasets-cli env` actually.., but here's my env:
- `datasets` version: 1.13.3
- Platform: Ubuntu 18.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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I_kwDODunzps49PbDa
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`cast_column` makes audio decoding fail
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[
"cc @anton-l @albertvillanova ",
"Thanks for reporting, @patrickvonplaten.\r\n\r\nI think the issue is related to mp3 resampling, not to `cast_column`.\r\n\r\nYou can check that `cast_column` works OK with non-mp3 audio files:\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\nds = load_dataset(\"arabic_speech_corpus\", split=\"train\")\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\nprint(ds[0][\"audio\"])\r\n```\r\n\r\nI'm fixing it."
] |
2021-10-15T13:36:58Z
|
2023-04-07T09:43:20Z
|
2021-10-15T15:38:30Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
After changing the sampling rate automatic decoding fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import datasets
ds = load_dataset("common_voice", "ab", split="train")
ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))
print(ds[0]["audio"]) # <- this fails currently
```
yields:
```
TypeError: forward() takes 2 positional arguments but 4 were given
```
## Expected results
no failure
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.13.2 (master)
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
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I_kwDODunzps49O8p5
| 3,094
|
Support loading a dataset from SQLite files
|
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[
"for reference Kaggle has a good number of open source datasets stored in sqlite\r\n\r\nAlternatively a tutorial or tool on how to convert from sqlite to parquet would be cool too",
"Hello, could we leverage [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) for this? \r\n\r\nThis would be basically the same as [`CSVBuilder`](https://github.com/huggingface/datasets/blob/7380140accf522a4363bb56c0b77a4190f49bed6/src/datasets/packaged_modules/csv/csv.py#L127)\r\n, but uses `pandas.read_sql(..., chunksize=1)` instead of `pandas.read_csv(..., iterator=True)`\r\n\r\nI'm happy to work on this :) \r\n\r\nself-assign"
] |
2021-10-15T10:58:41Z
|
2022-10-03T16:32:29Z
|
2022-10-03T16:32:29Z
|
MEMBER
| null | null | null | null |
As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files.
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"Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf = pd.read_json(buffer, lines=True)\r\n\r\nprint(df.shape[0]) # 0\r\n```\r\n\r\nSo we can't even fall back to Pandas in such cases.\r\n\r\nIt seems the only option is a script that recursively re-orders fields to enforce deterministic order:\r\n```python\r\nwith open(\"train.json\", \"r\") as fin:\r\n with open(\"train_reordered.json\", \"w\") as fout:\r\n for line in fin:\r\n obj_jsonl = json.loads(line.strip())\r\n fout.write(json.dumps(obj_jsonl, sort_keys=True) + \"\\n\")\r\n```",
"Fixed in #3575, so I'm closing this issue."
] |
2021-10-15T09:33:25Z
|
2022-04-10T14:06:29Z
|
2022-04-10T14:06:29Z
|
NONE
| null | null | null | null |
## Describe the bug
Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below.
If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine.
## Steps to reproduce the bug
Create two json files:
train.json
```
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
```
test.json
```
{"a": {"b": 1, "c": 2}}
{"a": {"b": 3, "c": 4}}
```
```python
from datasets import load_dataset
# Loading the files individually works (even though the keys in train.json don't have the same order)
load_dataset('json', data_files={"test": "test.json"})
load_dataset('json', data_files={"train": "train.json"})
# Loading both splits fails
load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
```
## Expected results
Loading both splits should not give an error whether the nested dicts are have the same order or not.
## Actual results
```
>>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
Using custom data configuration default-f1bc76fd07398c4c
Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 5.0.0
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I_kwDODunzps49Op1K
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`blog_authorship_corpus` is broken
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[
"Hi @fdtomasi, thanks for reporting.\r\n\r\nYou are right: the original host data URL does no longer exist.\r\n\r\nI've contacted the authors of the dataset to ask them if they host this dataset in another URL.",
"Hi, @fdtomasi, the URL is fixed.\r\n\r\nThe fix is already in our master branch and it will be accessible in our next release.\r\n\r\nIn the meantime, you can include the fix if you install the `datasets` library from the master branch:\r\n```\r\npip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```",
"Awesome thank you so much for the quick fix!"
] |
2021-10-15T09:20:40Z
|
2021-10-19T13:06:10Z
|
2021-10-19T12:50:39Z
|
NONE
| null | null | null | null |
## Describe the bug
The dataset `blog_authorship_corpus` is broken.
By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.
I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip).
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload')
```
## Expected results
No error.
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
/tmp/ipykernel_5237/1729238701.py in <module>
2 ds = load_dataset(
3 "blog_authorship_corpus", split="train",
----> 4 download_mode='force_redownload'
5 )
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
707 if verify_infos:
708 verify_checksums(
--> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
710 )
711
/opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip']
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.10
- PyArrow version: 5.0.0
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JNLPBA Dataset
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[
"# Steps to reproduce\r\n\r\nTo reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('jnlpba')\r\n\r\ndataset['train'].features['ner_tags']\r\n```\r\nOutput:\r\n```python\r\nSequence(feature=ClassLabel(num_classes=3, names=['O', 'B', 'I'], names_file=None, id=None), length=-1, id=None)\r\n```\r\n\r\n",
"Since I cannot create a branch here is the updated code:\r\n\r\n```python\r\n\r\n# coding=utf-8\r\n# Copyright 2020 HuggingFace Datasets Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Lint as: python3\r\n\"\"\"Introduction to the Bio-Entity Recognition Task at JNLPBA\"\"\"\r\n\r\nimport os\r\n\r\nimport datasets\r\n\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@inproceedings{kim2004introduction,\r\n title={Introduction to the bio-entity recognition task at JNLPBA},\r\n author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},\r\n booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},\r\n pages={70--75},\r\n year={2004},\r\n organization={Citeseer}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search\r\non MEDLINE using the MeSH terms \u0018human\u0019, \u0018blood cells\u0019 and \u0018transcription factors\u0019. From this search 2,000 abstracts\r\nwere selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.\r\nAmong the classes, 36 terminal classes were used to annotate the GENIA corpus.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004\"\r\n_TRAIN_URL = \"http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz\"\r\n_VAL_URL = 'http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz'\r\n\r\n\r\n_URLS = {\r\n \"train\": _TRAIN_URL,\r\n \"val\": _VAL_URL,\r\n}\r\n\r\n_TRAIN_DIRECTORY = \"Genia4ERtraining\"\r\n_VAL_DIRECTORY = \"Genia4ERtest\"\r\n\r\n_TRAIN_FILE = \"Genia4ERtask1.iob2\"\r\n_VAL_FILE = \"Genia4EReval1.iob2\"\r\n\r\n\r\nclass JNLPBAConfig(datasets.BuilderConfig):\r\n \"\"\"BuilderConfig for JNLPBA\"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"BuilderConfig for JNLPBA.\r\n Args:\r\n **kwargs: keyword arguments forwarded to super.\r\n \"\"\"\r\n super(JNLPBAConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass JNLPBA(datasets.GeneratorBasedBuilder):\r\n \"\"\"JNLPBA dataset.\"\"\"\r\n\r\n BUILDER_CONFIGS = [\r\n JNLPBAConfig(name=\"jnlpba\", version=datasets.Version(\"1.0.0\"), description=\"JNLPBA dataset\"),\r\n ]\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"id\": datasets.Value(\"string\"),\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"ner_tags\": datasets.Sequence(\r\n datasets.features.ClassLabel(\r\n names=[\r\n 'O',\r\n 'B-DNA',\r\n 'I-DNA', \r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein',\r\n ]\r\n )\r\n ),\r\n }\r\n ),\r\n supervised_keys=None,\r\n homepage=_HOMEPAGE,\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n \r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, \r\n gen_kwargs={\"filepath\": os.path.join(downloaded_files['train'], _TRAIN_FILE)}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, \r\n gen_kwargs={\"filepath\": os.path.join(downloaded_files['val'], _VAL_FILE)})\r\n ]\r\n \r\n\r\n def _generate_examples(self, filepath):\r\n logger.info(\"⏳ Generating examples from = %s\", filepath)\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n guid = 0\r\n tokens = []\r\n ner_tags = []\r\n for line in f:\r\n if line.startswith('###'):\r\n continue\r\n if line == '' or line == '\\n':\r\n if tokens:\r\n yield guid, {\r\n \"id\": str(guid),\r\n \"tokens\": tokens,\r\n \"ner_tags\": ner_tags,\r\n }\r\n guid += 1\r\n tokens = []\r\n ner_tags = []\r\n else:\r\n # tokens are tab separated\r\n splits = line.split(\"\\t\")\r\n #print(splits)\r\n #print(len(splits))\r\n if len(splits) < 2:\r\n splits = splits[0].split()\r\n tokens.append(splits[0])\r\n ner_tags.append(splits[1].rstrip())\r\n # last example\r\n yield guid, {\r\n \"id\": str(guid),\r\n \"tokens\": tokens,\r\n \"ner_tags\": ner_tags,\r\n }\r\n```"
] |
2021-10-15T01:16:02Z
|
2021-10-22T08:23:57Z
|
2021-10-22T08:23:57Z
|
NONE
| null | null | null | null |
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I. The correct entities from the original data file are:
['O',
'B-DNA',
'I-DNA',
'B-RNA',
'I-RNA',
'B-cell_line',
'I-cell_line',
'B-cell_type',
'I-cell_type',
'B-protein',
'I-protein']
## Actual results
The dataset loader script needs to include the following NER names:
['O',
'B-DNA',
'I-DNA',
'B-RNA',
'I-RNA',
'B-cell_line',
'I-cell_line',
'B-cell_type',
'I-cell_type',
'B-protein',
'I-protein']
And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
|
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| 1,026,780,469
|
I_kwDODunzps49M201
| 3,087
|
Removing label column in a text classification dataset yields to errors
|
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2021-10-14T20:12:50Z
|
2021-10-15T10:11:04Z
|
2021-10-15T10:11:04Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
This looks like #3059 but it's not linked to the cache this time. Removing the `label` column from a text classification dataset and then performing any processing will result in an error.
To reproduce:
```py
from datasets import load_dataset
from transformers import AutoTokenizer
raw_datasets = load_dataset("imdb")
raw_datasets = raw_datasets.remove_columns("label")
model_checkpoint = "distilbert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
context_length = 128
def tokenize_pad_and_truncate(texts):
return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length)
tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True)
```
Traceback:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-ba61bb32f786> in <module>
12 return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length)
13
---> 14 tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True)
~/git/datasets/src/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)
500 desc=desc,
501 )
--> 502 for k, dataset in self.items()
503 }
504 )
~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0)
500 desc=desc,
501 )
--> 502 for k, dataset in self.items()
503 }
504 )
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2051 new_fingerprint=new_fingerprint,
2052 disable_tqdm=disable_tqdm,
-> 2053 desc=desc,
2054 )
2055 else:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2243 if os.path.exists(cache_file_name) and load_from_cache_file:
2244 logger.warning("Loading cached processed dataset at %s", cache_file_name)
-> 2245 info = self.info.copy()
2246 info.features = features
2247 info.task_templates = None
~/git/datasets/src/datasets/info.py in copy(self)
278
279 def copy(self) -> "DatasetInfo":
--> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
281
282
~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes)
~/git/datasets/src/datasets/info.py in __post_init__(self)
177 for idx, template in enumerate(self.task_templates):
178 if isinstance(template, TextClassification):
--> 179 labels = self.features[template.label_column].names
180 self.task_templates[idx] = TextClassification(
181 text_column=template.text_column, label_column=template.label_column, labels=labels
KeyError: 'label'
```
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VisibleDeprecationWarning when using `set_format("numpy")`
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[
"I just opened a PR and I verified that the code you provided doesn't show any deprecation warning :)"
] |
2021-10-14T13:53:01Z
|
2021-10-22T16:04:14Z
|
2021-10-22T16:04:14Z
|
MEMBER
| null | null | null | null |
Code to reproduce:
```
from datasets import load_dataset
dataset = load_dataset("glue", "mnli")
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')
def tokenize_function(dataset):
return tokenizer(dataset['premise'])
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features)
tokenized_datasets.set_format("numpy")
tokenized_datasets['train'][5:8]
```
Outputs:
```
python3.9/site-packages/datasets/formatting/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return np.array(array, copy=False, **self.np_array_kwargs)
```
|
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| 1,026,397,062
|
I_kwDODunzps49LZOG
| 3,083
|
Datasets with Audio feature raise error when loaded from cache due to _resampler parameter
|
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[] |
2021-10-14T13:23:53Z
|
2021-10-14T15:13:40Z
|
2021-10-14T15:13:40Z
|
MEMBER
| null | null | null | null |
## Describe the bug
As reported by @patrickvonplaten, when loaded from the cache, datasets containing the Audio feature raise TypeError.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# load first time works
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
# load from cache breaks
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
## Actual results
```
TypeError: __init__() got an unexpected keyword argument '_resampler'
```
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I_kwDODunzps49LVNS
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|
Error related to timeout keyword argument
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[] |
2021-10-14T13:10:58Z
|
2021-10-14T14:39:51Z
|
2021-10-14T14:39:51Z
|
MEMBER
| null | null | null | null |
## Describe the bug
As reported by @patrickvonplaten, a TypeError is raised when trying to load a dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
## Actual results
```
TypeError: dataset_info() got an unexpected keyword argument 'timeout'
```
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| 3,076
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2021-10-14T08:29:27Z
|
2021-10-14T09:14:55Z
|
2021-10-14T09:14:55Z
|
MEMBER
| null | null | null | null |
## Describe the bug
As reported by @sgugger, after last release, exception is thrown when loading a metric.
## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("squad_v2")
```
## Actual results
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-1-e612a8cab787> in <module>
1 from datasets import load_metric
----> 2 metric = load_metric("squad_v2")
d:\projects\huggingface\datasets\src\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, script_version, **metric_init_kwargs)
1336 )
1337 revision = script_version
-> 1338 metric_module = metric_module_factory(
1339 path, revision=revision, download_config=download_config, download_mode=download_mode
1340 ).module_path
d:\projects\huggingface\datasets\src\datasets\load.py in metric_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, **download_kwargs)
1237 if not isinstance(e1, FileNotFoundError):
1238 raise e1 from None
-> 1239 raise FileNotFoundError(
1240 f"Couldn't find a metric script at {relative_to_absolute_path(combined_path)}. "
1241 f"Metric '{path}' doesn't exist on the Hugging Face Hub either."
FileNotFoundError: Couldn't find a metric script at D:\projects\huggingface\datasets\squad_v2\squad_v2.py. Metric 'squad_v2' doesn't exist on the Hugging Face Hub either.
```
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I_kwDODunzps49IzjF
| 3,073
|
Import error installing with ppc64le
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[
"This seems to be an issue with importing PyArrow so I posted the problem [here](https://issues.apache.org/jira/browse/ARROW-14323), and I'm closing this issue.\r\n"
] |
2021-10-13T21:37:23Z
|
2021-10-14T16:35:46Z
|
2021-10-14T16:33:28Z
|
NONE
| null | null | null | null |
## Describe the bug
Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library.
```
python
Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Illegal instruction (core dumped)
```
Error when importing
`Illegal instruction (core dumped)`
## Steps to reproduce the bug
I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge
```
conda create --name transformers_py36_v2 python=3.6
conda activate transformers_py36_v2
conda install datasets
```
## Tracebacks
conda create --name transformers_py36_v2 python=3.6
```
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- python=3.6
The following NEW packages will be INSTALLED:
_libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge
_openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu
ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0
certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0
ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2
libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4
libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11
libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11
libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11
libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013
ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4
openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0
pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0
python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython
readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0
setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0
sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2
tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1
wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1
xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1
zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate transformers_py36_v2
#
# To deactivate an active environment, use
#
# $ conda deactivate
```
conda activate transformers_py36_v2
conda install datasets
```
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- datasets
The following NEW packages will be INSTALLED:
abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0
aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0
arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu
async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000
attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0
aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0
aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0
aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13
aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0
aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7
aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3
brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001
bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4
c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0
cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1
chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1
colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0
cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0
dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2
datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1
dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0
filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0
fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0
gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004
glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0
grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2
huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0
idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0
idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0
importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0
importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0
krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2
libblas conda-forge/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas
libbrotlicommon conda-forge/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5
libbrotlidec conda-forge/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5
libbrotlienc conda-forge/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5
libcblas conda-forge/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas
libcurl conda-forge/linux-ppc64le::libcurl-7.79.1-he415e40_1
libedit conda-forge/linux-ppc64le::libedit-3.1.20191231-h41a240f_2
libev conda-forge/linux-ppc64le::libev-4.33-h6eb9509_1
libevent conda-forge/linux-ppc64le::libevent-2.1.10-h97db324_4
libgfortran-ng conda-forge/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11
libgfortran5 conda-forge/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11
liblapack conda-forge/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas
libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1
libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1
libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0
libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2
libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1
libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0
lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1
multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0
multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0
numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1
orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0
packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0
pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0
parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2
pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu
pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2
pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0
pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0
pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3
python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0
python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0
python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m
pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0
pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1
re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0
requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0
s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0
six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3
tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0
typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0
typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0
urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0
xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3
yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0
yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2
zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0
zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0
The following packages will be UPDATED:
certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Red Hat Enterprise Linux 8.2 (Ootpa)
- Python version: 3.6
- PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge
Any help would be appreciated! I've been struggling on installing datasets on this machine.
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Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder
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] | null |
[
"Hi @zixiliuUSC, \r\n\r\nAs explained in the documentation (https://huggingface.co/docs/datasets/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:\r\n```python\r\nds = load_dataset('json', data_files='my_file.json')\r\n```"
] |
2021-10-13T07:32:10Z
|
2021-10-13T08:27:04Z
|
2021-10-13T08:27:03Z
|
NONE
| null | null | null | null |
## Adding a Dataset
- **Name:** text, json, csv
- **Description:** I am developing a customized dataset loading script. The problem is mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?
|
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| 1,024,818,680
|
I_kwDODunzps49FX34
| 3,069
|
CI fails on Windows with FileNotFoundError when stting up s3_base fixture
|
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[] |
2021-10-13T05:52:26Z
|
2021-10-13T08:05:49Z
|
2021-10-13T06:49:48Z
|
MEMBER
| null | null | null | null |
## Describe the bug
After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321
Error summary:
```
ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF...
ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo...
```
Stack trace:
```
______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
@pytest.fixture()
def s3_base():
# writable local S3 system
import shlex
import subprocess
# Mocked AWS Credentials for moto.
old_environ = os.environ.copy()
os.environ.update(S3_FAKE_ENV_VARS)
> proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port))
tests\s3_fixtures.py:32:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\tools\miniconda3\lib\subprocess.py:729: in __init__
restore_signals, start_new_session)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <subprocess.Popen object at 0x0000012BB8A4B908>
args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None
close_fds = True, pass_fds = (), cwd = None, env = None
startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630>
creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1
c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True
unused_start_new_session = False
def _execute_child(self, args, executable, preexec_fn, close_fds,
pass_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
unused_restore_signals, unused_start_new_session):
"""Execute program (MS Windows version)"""
assert not pass_fds, "pass_fds not supported on Windows."
if not isinstance(args, str):
args = list2cmdline(args)
# Process startup details
if startupinfo is None:
startupinfo = STARTUPINFO()
if -1 not in (p2cread, c2pwrite, errwrite):
startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES
startupinfo.hStdInput = p2cread
startupinfo.hStdOutput = c2pwrite
startupinfo.hStdError = errwrite
if shell:
startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = _winapi.SW_HIDE
comspec = os.environ.get("COMSPEC", "cmd.exe")
args = '{} /c "{}"'.format (comspec, args)
# Start the process
try:
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
# no special security
None, None,
int(not close_fds),
creationflags,
env,
os.fspath(cwd) if cwd is not None else None,
> startupinfo)
E FileNotFoundError: [WinError 2] The system cannot find the file specified
C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError
```
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| 1,023,900,075
|
I_kwDODunzps49B3mr
| 3,064
|
Make `interleave_datasets` more robust
|
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[
"Hi @lhoestq Any response on this issue?",
"Hi ! Sorry for the late response\r\n\r\nI agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop=\"first_exhausted\"` (default) or `stop=\"all_exhausted\"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)\r\n\r\nAlso one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.\r\n\r\nRegarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. ",
"@sbmaruf I just notice that (1)`interleave_datasets` only samples indices once and reuse for all epochs, and (2) it's limited by the smallest dataset. Do you figure out an alternative way to achieve the same purpose?"
] |
2021-10-12T14:34:53Z
|
2022-07-30T08:47:26Z
| null |
NONE
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration.
It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.
**Describe the solution you'd like**
For `interleave_datasets` module,
- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.
- [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point.
- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`
Note: I'm new to `datasets` api. May be these features are already there in the datasets.
Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.
@lhoestq
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|
I_kwDODunzps49ArfJ
| 3,063
|
Windows CI is unable to test streaming properly because of SSL issues
|
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[
"I think this problem is already fixed:\r\n```python\r\nIn [4]: import fsspec\r\n ...:\r\n ...: url = \"https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes\"\r\n ...:\r\n ...: fsspec.open(url).open()\r\nOut[4]: <File-like object HTTPFileSystem, https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattribu\r\n```",
"No I'm still having this issue on my windows, and so does the CI"
] |
2021-10-12T09:33:40Z
|
2022-08-24T14:59:29Z
|
2022-08-24T14:59:29Z
|
MEMBER
| null | null | null | null |
In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443
The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.
to reproduce on windows:
```python
import fsspec
# use any URL to a file in a dataset repo
url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"
fsspec.open(url).open()
```
raises
```python
FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes
```
because of
```python
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]
```
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| 1,023,103,119
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I_kwDODunzps48-1CP
| 3,061
|
Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)
|
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[
"@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.",
"Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as an actual parameters instead of kwargs to make it clearer"
] |
2021-10-11T20:49:49Z
|
2021-10-22T09:34:10Z
| null |
NONE
| null | null | null | null |
**A clear and concise description of what you want to happen.**
It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.
**Describe alternatives you've considered**
By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?
**Additional context**
Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=`
@sgugger @bhavitvyamalik
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| 1,022,936,396
|
I_kwDODunzps48-MVM
| 3,060
|
load_dataset('openwebtext') yields "Compressed file ended before the end-of-stream marker was reached"
|
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[
"Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.",
"I close this issue for the moment. Feel free to re-open it again if the problem persists."
] |
2021-10-11T17:05:27Z
|
2021-10-28T05:52:21Z
|
2021-10-28T05:52:21Z
|
NONE
| null | null | null | null |
## Describe the bug
When I try `load_dataset('openwebtext')`, I receive a "EOFError: Compressed file ended before the end-of-stream marker was reached" error.
## Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('openwebtext')
```
## Expected results
I expect the `dataset` variable to be properly constructed.
## Actual results
```
File "/home/rschaef/CoCoSci-Language-Distillation/distillation_v2/ratchet_learning/tasks/base.py", line 37, in create_dataset
dataset_str,
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/load.py", line 1117, in load_dataset
use_auth_token=use_auth_token,
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 637, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 704, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rschaef/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1/openwebtext.py", line 61, in _split_generators
dl_dir = dl_manager.download_and_extract(_URL)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 261, in extract
partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 197, in map_nested
return function(data_struct)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 316, in cached_path
output_path, force_extract=download_config.force_extract
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 40, in extract
self.extractor.extract(input_path, output_path, extractor=extractor)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 179, in extract
return extractor.extract(input_path, output_path)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 53, in extract
tar_file.extractall(output_path)
File "/usr/lib/python3.6/tarfile.py", line 2010, in extractall
numeric_owner=numeric_owner)
File "/usr/lib/python3.6/tarfile.py", line 2052, in extract
numeric_owner=numeric_owner)
File "/usr/lib/python3.6/tarfile.py", line 2122, in _extract_member
self.makefile(tarinfo, targetpath)
File "/usr/lib/python3.6/tarfile.py", line 2171, in makefile
copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
File "/usr/lib/python3.6/tarfile.py", line 249, in copyfileobj
buf = src.read(bufsize)
File "/usr/lib/python3.6/lzma.py", line 200, in read
return self._buffer.read(size)
File "/usr/lib/python3.6/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/usr/lib/python3.6/_compression.py", line 99, in read
raise EOFError("Compressed file ended before the "
python-BaseException
EOFError: Compressed file ended before the end-of-stream marker was reached
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0
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I_kwDODunzps4889S4
| 3,058
|
Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.
|
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[
"Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https://github.com/huggingface/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` ?",
"Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?"
] |
2021-10-11T11:54:59Z
|
2022-01-19T14:03:49Z
|
2022-01-19T14:03:49Z
|
NONE
| null | null | null | null |
## Describe the bug
I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.
## Steps to reproduce the bug
Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.
## Expected results
The data batchs are fetched from the data loader and train.
## Actual results
The first time to fetch data batch occurs error.
`Traceback (most recent call last):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
tensor = as_tensor(value)
ValueError: too many dimensions 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "src/original_run_mlm_no_trainer.py", line 528, in <module>
main()
File "src/original_run_mlm_no_trainer.py", line 488, in main
for step, batch in enumerate(train_dataloader):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__
for batch in super().__iter__():
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__
return self.torch_call(features)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call
batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.6
- PyArrow version: 5.0.0
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| 1,022,508,315
|
I_kwDODunzps488j0b
| 3,057
|
Error in per class precision computation
|
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[
"Hi @tidhamecha2, thanks for reporting.\r\n\r\nIndeed, we fixed this issue just one week ago: #3008\r\n\r\nThe fix will be included in our next version release.\r\n\r\nIn the meantime, you can incorporate the fix by installing `datasets` from the master branch:\r\n```\r\npip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```"
] |
2021-10-11T10:05:19Z
|
2021-10-11T10:17:44Z
|
2021-10-11T10:16:16Z
|
NONE
| null | null | null | null |
## Describe the bug
When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`
## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
precision_metric = load_metric("precision")
predictions = [0, 2, 1, 0, 0, 1]
references = [0, 1, 2, 0, 1, 2]
results = precision_metric.compute(predictions=predictions, references=references, average=None)
```
## Expected results
` {'precision': array([0.66666667, 0. , 0. ])}`
as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py
## Actual results
```
output = self._compute(predictions=predictions, references=references, **kwargs)
File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute
sample_weight=sample_weight,
ValueError: can only convert an array of size 1 to a Python scalar
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.6.9
- PyArrow version: 5.0.0
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| 1,022,319,238
|
I_kwDODunzps4871qG
| 3,055
|
CI test suite fails after meteor metric update
|
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2021-10-11T06:37:12Z
|
2021-10-11T07:30:31Z
|
2021-10-11T07:30:31Z
|
MEMBER
| null | null | null | null |
## Describe the bug
CI test suite fails: https://app.circleci.com/pipelines/github/huggingface/datasets/8110/workflows/f059ba43-9154-4632-bebb-82318447ddc9/jobs/50010
Stack trace:
```
___________________ LocalMetricTest.test_load_metric_meteor ____________________
[gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6
self = <tests.test_metric_common.LocalMetricTest testMethod=test_load_metric_meteor>
metric_name = 'meteor'
def test_load_metric(self, metric_name):
doctest.ELLIPSIS_MARKER = "[...]"
metric_module = importlib.import_module(datasets.load.prepare_module(os.path.join("metrics", metric_name))[0])
metric = datasets.load.import_main_class(metric_module.__name__, dataset=False)
# check parameters
parameters = inspect.signature(metric._compute).parameters
self.assertTrue("predictions" in parameters)
self.assertTrue("references" in parameters)
self.assertTrue(all([p.kind != p.VAR_KEYWORD for p in parameters.values()])) # no **kwargs
# run doctest
with self.patch_intensive_calls(metric_name, metric_module.__name__):
with self.use_local_metrics():
> results = doctest.testmod(metric_module, verbose=True, raise_on_error=True)
tests/test_metric_common.py:75:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1951: in testmod
runner.run(test)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1839: in run
r = DocTestRunner.run(self, test, compileflags, out, False)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1476: in run
return self.__run(test, compileflags, out)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1382: in __run
exception)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <doctest.DebugRunner object at 0x7f4c26bd3da0>
out = <built-in method write of _io.TextIOWrapper object at 0x7f51a21852d0>
test = <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Mete...ets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)>
example = <doctest.Example object at 0x7f4c26bd3eb8>
exc_info = (<class 'TypeError'>, TypeError('"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): It is a guide to action which ensures that the military always obeys the commands of the party',), <traceback object at 0x7f4cd01afec8>)
def report_unexpected_exception(self, out, test, example, exc_info):
> raise UnexpectedException(test, example, exc_info)
E doctest.UnexpectedException: <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Meteor from /tmp/pytest-of-circleci/pytest-0/popen-gw1/cache/modules/datasets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)>
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1845: UnexpectedException
```
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| 1,022,076,905
|
I_kwDODunzps4866fp
| 3,053
|
load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type
|
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[] | null |
[
"I encountered the same bug using different datasets.\r\nany suggestions?",
"+1, can reproduce here!",
"I get the same error\r\nPlatform: Windows 10\r\nPython: python 3.8.8\r\nPyArrow: 5.0",
"I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value(\"int8\"))`, but the actual values can be well outside the max range for 8-bit integers.\r\n\r\nI worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).",
"Addressed in https://huggingface.co/datasets/the_pile_openwebtext2/discussions/4"
] |
2021-10-10T19:55:21Z
|
2023-02-24T14:02:20Z
|
2023-02-24T14:02:20Z
|
NONE
| null | null | null | null |
## Describe the bug
When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type`
## Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset('the_pile_openwebtext2')
```
## Expected results
Should download the dataset, convert it to an arrow file, and return a working Dataset object.
## Actual results
The download works, but conversion to the arrow file fails as follows:
```
>>> ds = datasets.load_dataset('the_pile_openwebtext2')
Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB
, post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open
webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25...
Traceback (most recent call last):
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133,
in _prepare_split
writer.write(example, key)
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
366, in write
self.write_examples_on_file()
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
311, in write_examples_on_file
pa_array = pa.array(typed_sequence)
File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
115, in __arrow_array__
out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)
File "pyarrow/array.pxi", line 305, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
```
- Platform: Ubuntu 20.04
- Python version: python 3.9
- PyArrow version: 3.0.0
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I_kwDODunzps486aJz
| 3,052
|
load_dataset cannot download the data and hangs on forever if cache dir specified
|
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[
"Issue was environment inconsistency, updating packages did the trick\r\n\r\n`conda install -c huggingface -c conda-forge datasets`\r\n\r\n> Collecting package metadata (current_repodata.json): done\r\n> Solving environment: |\r\n> The environment is inconsistent, please check the package plan carefully\r\n> The following packages are causing the inconsistency:\r\n> \r\n> - conda-forge/noarch::datasets==1.12.1=pyhd8ed1ab_1\r\n> - conda-forge/win-64::multiprocess==0.70.12.2=py38h294d835_0\r\n> done\r\n> \r\n> Package Plan\r\n> \r\n> environment location: C:\\xxx\\anaconda3\\envs\\UnBias-94-1\r\n> \r\n> added / updated specs:\r\n> - datasets\r\n> \r\n> \r\n> The following NEW packages will be INSTALLED:\r\n> \r\n> dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n> \r\n> The following packages will be UPDATED:\r\n> \r\n> ca-certificates pkgs/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0\r\n> certifi pkgs/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0\r\n> \r\n> The following packages will be SUPERSEDED by a higher-priority channel:\r\n> "
] |
2021-10-10T10:31:36Z
|
2021-10-11T10:57:09Z
|
2021-10-11T10:56:36Z
|
NONE
| null | null | null | null |
## Describe the bug
After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.
Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out. :(
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir)
```
Note that exact same code without specifying _cache_dir_ argument works perfectly fine.
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train')
```
## Expected results
Downloads the dataset and cache is handled in the _cache_dir_ directory
## Actual results
Data download keeps hanging on forever, **NO TRACEBACK**!
## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
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I_kwDODunzps486DpK
| 3,051
|
Non-Matching Checksum Error with crd3 dataset
|
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[
"I got the same error for another dataset (`multi_woz_v22`):\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']\r\n```",
"I'm seeing the same issue as @RylanSchaeffer:\r\nPython 3.7.11, macOs 11.4\r\ndatasets==1.14.0\r\n\r\nfails on:\r\n```python\r\ndataset = datasets.load_dataset(\"multi_woz_v22\")\r\n```"
] |
2021-10-10T01:32:43Z
|
2022-03-15T15:54:26Z
|
2022-03-15T15:54:26Z
|
NONE
| null | null | null | null |
## Describe the bug
When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown.
## Steps to reproduce the bug
```python
dataset = load_dataset('crd3', split='train')
```
## Expected results
I expect no error to be thrown.
## Actual results
A non-matching checksum error is thrown.
```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip']
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0
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I_kwDODunzps485vkY
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|
TimeoutError during streaming
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2021-10-09T18:06:51Z
|
2021-10-11T09:35:38Z
|
2021-10-11T09:35:38Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I got a TimeoutError after streaming for about 10h.
## Steps to reproduce the bug
Very long code but we could do a test of streaming indefinitely data, though error may take a while to appear.
## Expected results
This error was not expected in the code which considers only `ClientError` but not `TimeoutError`.
See [this line](https://github.com/huggingface/datasets/blob/2814fbd0e18150be409f10804670e98d9ecb87d4/src/datasets/utils/streaming_download_manager.py#L129).
Based on the traceback, it looks like the `TimeoutError` was not captured.
## Actual results
```
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 25, in _runner
result[0] = await coro
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 614, in async_fetch_range
out = await r.read()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1032, in read
self._body = await self.content.read()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 370, in read
block = await self.readany()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 392, in readany
await self._wait("readany")
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 306, in _wait
await waiter
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/helpers.py", line 656, in __exit__
raise asyncio.TimeoutError from None
asyncio.exceptions.TimeoutError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 1027, in <module>
main()
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 991, in main
for batch in tqdm(
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/tqdm/std.py", line 1180, in __iter__
for obj in iterable:
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 376, in data_loader_streaming
for item in dataset:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in __iter__
key_examples_list = [(key, example)] + [
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in <listcomp>
key_examples_list = [(key, example)] + [
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 176, in __iter__
for key, example in iterator:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 225, in __iter__
for x in self.ex_iterable:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 99, in __iter__
for key, example in self.generate_examples_fn(**kwargs_with_shuffled_shards):
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 287, in wrapper
for key, table in generate_tables_fn(**kwargs):
File "/home/koush/datasets/src/datasets/packaged_modules/json/json.py", line 107, in _generate_tables
batch = f.read(self.config.chunksize)
File "/home/koush/datasets/src/datasets/utils/streaming_download_manager.py", line 126, in read_with_retries
out = read(*args, **kwargs)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 572, in read
return super().read(length)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/spec.py", line 1533, in read
out = self.cache._fetch(self.loc, self.loc + length)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/caching.py", line 390, in _fetch
self.cache = self.fetcher(start, bend)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 91, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 69, in sync
raise FSTimeoutError from return_result
fsspec.exceptions.FSTimeoutError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.4.0-1043-gcp-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 5.0.0
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I_kwDODunzps485ugd
| 3,048
|
Identify which shard data belongs to
|
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[
"Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch."
] |
2021-10-09T17:46:35Z
|
2021-10-09T20:24:17Z
| null |
CONTRIBUTOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
I'm training on a large dataset made of multiple sub-datasets.
During training I can observe some jumps in loss which may correspond to different shards.
My suspicion is that either:
* some of the sub-datasets are harder for the model than others
* some of the sub-datasets are not formatted properly
I'd like to identify which shards correspond to those jumps.
**Describe the solution you'd like**
It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).
This should be supported both in local and streaming mode.
**Describe alternatives you've considered**
A fix would be for me to add myself details (shard id, sample id) as part of each data sample.
The inconvenient is that it requires users to process/reupload every dataset when they need this feature.
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I_kwDODunzps484Lno
| 3,047
|
Loading from cache a dataset for LM built from a text classification dataset sometimes errors
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[
"This has been fixed in 1.15, let me know if you still have this issue"
] |
2021-10-08T18:23:11Z
|
2021-11-03T17:13:08Z
|
2021-11-03T17:13:08Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Yes, I know, that description sucks. So the problem is arising in the course when we build a masked language modeling dataset using the IMDB dataset. To reproduce (or try since it's a bit fickle).
Create a dataset for masled-language modeling from the IMDB dataset.
```python
from datasets import load_dataset
from transformers import Autotokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased)
imdb_dataset = load_dataset("imdb", split="train")
def tokenize_function(examples):
return tokenizer(examples["text"])
tokenized_dataset = imdb_dataset.map(
tokenize_function, batched=True, remove_columns=["text", "label"]
)
chunk_size = 128
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
# Compute length of concatenated texts
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the last chunk if it's smaller than chunk_size
total_length = (total_length // chunk_size) * chunk_size
# Split by chunks of max_len.
result = {
k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]
for k, t in concatenated_examples.items()
}
# Create a new labels column
result["labels"] = result["input_ids"].copy()
return result
lm_dataset = tokenized_dataset.map(group_texts, batched=True)
```
Until now, all is well. The problem comes when you re-execute that code, more specifically:
```python
tokenized_dataset = imdb_dataset.map(
tokenize_function, batched=True, remove_columns=["text", "label"]
)
lm_dataset = tokenized_dataset.map(group_texts, batched=True)
```
Try several times if the bug doesn't appear instantly, or just each line at a time, ideally in a notebook/Colab and you should get at some point:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-40-357a56ee3d53> in <module>
----> 1 lm_dataset = tokenized_dataset.map(group_texts, batched=True)
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1947 new_fingerprint=new_fingerprint,
1948 disable_tqdm=disable_tqdm,
-> 1949 desc=desc,
1950 )
1951 else:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
424 }
425 # apply actual function
--> 426 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
427 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
428 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2138 if os.path.exists(cache_file_name) and load_from_cache_file:
2139 logger.warning("Loading cached processed dataset at %s", cache_file_name)
-> 2140 info = self.info.copy()
2141 info.features = features
2142 return Dataset.from_file(cache_file_name, info=info, split=self.split)
~/git/datasets/src/datasets/info.py in copy(self)
278
279 def copy(self) -> "DatasetInfo":
--> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
281
282
~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes)
~/git/datasets/src/datasets/info.py in __post_init__(self)
177 for idx, template in enumerate(self.task_templates):
178 if isinstance(template, TextClassification):
--> 179 labels = self.features[template.label_column].names
180 self.task_templates[idx] = TextClassification(
181 text_column=template.text_column, label_column=template.label_column, labels=labels
KeyError: 'label'
```
It seems that when loading the cache, the dataset tries to access some kind of text classification template (which I imagine comes from the original dataset) and to look at a key that has since been removed.
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I_kwDODunzps482TyS
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Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`
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[
"Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.\r\n\r\nCurrently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.\r\n\r\nHowever it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.\r\n\r\nSee the documentation about their `hash_funcs` here: https://docs.streamlit.io/library/advanced-features/caching#the-hash_funcs-parameter\r\n\r\nHere is the example they give:\r\n\r\n```python\r\nclass FileReference:\r\n def __init__(self, filename):\r\n self.filename = filename\r\n\r\ndef hash_file_reference(file_reference):\r\n filename = file_reference.filename\r\n return (filename, os.path.getmtime(filename))\r\n\r\n@st.cache(hash_funcs={FileReference: hash_file_reference})\r\ndef func(file_reference):\r\n ...\r\n```",
"My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.\r\n\r\n@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. ",
"Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like \"000001_of_000008\" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.\r\n\r\nIs there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch. ",
"> ## Expected results\n> In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache` (\"=== Cache does not exist! ====\" should not be printed). When the cache is successfully created, `map()` is called only one time.\n> \n> ## Actual results\n> In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing \"=== Cache does not exist! ====\"). Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.\n\nIn your example\n\n`cache_path = \"~/.cache/huggingface/datasets/json/.../cache-3b163736cf4505085d8b5f9b4c266c26.arrow\"`\n\nbut\n\n```console\n$ tree~/.cache/huggingface/datasets/json/.../\n~/.cache/huggingface/datasets/json/.../\n├── cache-3b163736cf4505085d8b5f9b4c266c26_00000_of_00002.arrow\n├── cache-3b163736cf4505085d8b5f9b4c266c26_00001_of_00002.arrow\n```\n\nWhen `num_proc > 1`, the cache files are sharded and not saved under `cache_path`. Instead, a suffix appended, and so it is expected that `not os.path.exists(cache_path)` and that `\"=== Cache does not exist! ====\"`.\n\nYou can see there isn't a 2nd progress bar, also, so it is definitely using the cache on the second call to `process_dataset_with_cache` with both `num_proc=1` and `num_proc=2`."
] |
2021-10-08T09:07:10Z
|
2025-03-04T07:16:00Z
| null |
NONE
| null | null | null | null |
## Describe the bug
Caching does not work when using `Dataset.map()` with:
1. a function that cannot be deterministically fingerprinted
2. `num_proc>1`
3. using a custom fingerprint set with the argument `new_fingerprint`.
This means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the expected behaviour. The example can easily be translated into a unit test.
I have a fix and will submit a pull request asap.
## Steps to reproduce the bug
```python
import hashlib
import json
import os
from typing import Dict, Any
import numpy as np
from datasets import load_dataset, Dataset
Batch = Dict[str, Any]
filename = 'example.json'
class Transformation():
"""A transformation with a random state that cannot be fingerprinted"""
def __init__(self):
self.state = np.random.random()
def __call__(self, batch: Batch) -> Batch:
batch['x'] = [np.random.random() for _ in batch['x']]
return batch
def generate_dataset():
"""generate a simple dataset"""
rgn = np.random.RandomState(24)
data = {
'data': [{'x': float(y), 'y': -float(y)} for y in
rgn.random(size=(1000,))]}
if not os.path.exists(filename):
with open(filename, 'w') as f:
f.write(json.dumps(data))
return filename
def process_dataset_with_cache(num_proc=1, remove_cache=False,
cache_expected_to_exist=False):
# load the generated dataset
dset: Dataset = next(
iter(load_dataset('json', data_files=filename, field='data').values()))
new_fingerprint = hashlib.md5("static-id".encode("utf8")).hexdigest()
# get the expected cached path
cache_path = dset._get_cache_file_path(new_fingerprint)
if remove_cache and os.path.exists(cache_path):
os.remove(cache_path)
# check that the cache exists, and print a statement
# if was actually expected to exist
cache_exist = os.path.exists(cache_path)
print(f"> cache file exists={cache_exist}")
if cache_expected_to_exist and not cache_exist:
print("=== Cache does not exist! ====")
# apply the transformation with the new fingerprint
dset = dset.map(
Transformation(),
batched=True,
num_proc=num_proc,
new_fingerprint=new_fingerprint,
desc="mapping dataset with transformation")
generate_dataset()
for num_proc in [1, 2]:
print(f"# num_proc={num_proc}, first pass")
# first pass to generate the cache (always create a new cache here)
process_dataset_with_cache(remove_cache=True,
num_proc=num_proc,
cache_expected_to_exist=False)
print(f"# num_proc={num_proc}, second pass")
# second pass, expects the cache to exist
process_dataset_with_cache(remove_cache=False,
num_proc=num_proc,
cache_expected_to_exist=True)
os.remove(filename)
```
## Expected results
In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache` ("=== Cache does not exist! ====" should not be printed).
When the cache is successfully created, `map()` is called only one time.
## Actual results
In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing "=== Cache does not exist! ====").
Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.
## Environment info
- `datasets` version: 1.12.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 5.0.0
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I_kwDODunzps48z8_S
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|
Add PASS dataset
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2021-10-07T16:43:43Z
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2022-01-20T16:50:47Z
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2022-01-20T16:50:47Z
|
CONTRIBUTOR
| null | null | null | null |
## Adding a Dataset
- **Name:** PASS
- **Description:** An ImageNet replacement for self-supervised pretraining without humans
- **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset
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[
"Hi,\r\n\r\nthe `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard/slice of the dataset.",
"That works! Thansk!\r\n\r\nMight be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)",
"I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?",
"That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`",
"+1 on this !"
] |
2021-10-06T17:08:47Z
|
2021-11-02T15:41:08Z
|
2021-11-02T15:41:08Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.
## Steps to reproduce the bug
E.g. run the following:
```python
from datasets import load_dataset, save_to_disk
nlp = load_dataset("glue", "mnli", split="train")
nlp.save_to_disk("full")
nlp = nlp.select(range(100))
nlp.save_to_disk("dummy")
```
Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO.
## Expected results
IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
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Protect master branch to force contributions via Pull Requests
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[
"It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way ?",
"you can if you're an admin of the repo",
"This is done. Now the master branch is protected:\r\n- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request\r\n - Required number of approvals before merging: 1 \r\n- [x] Require linear history: prevent merge commits from being pushed\r\n- [x] These requirements are not enforced for administrators\r\n- [x] Additionally, the master branch is also protected against deletion and force pushes\r\n\r\nCC: @lhoestq @julien-c @thomwolf "
] |
2021-10-06T07:34:17Z
|
2021-10-07T06:51:47Z
|
2021-10-07T06:49:52Z
|
MEMBER
| null | null | null | null |
In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.
- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution
- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions)
Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.
Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.
The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.
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`load_dataset` does not work with uploaded arrow file
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[
"Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)",
"cc @LysandreJik maybe we can solve this at the same time as adding `push_to_hub`"
] |
2021-10-05T20:15:10Z
|
2021-10-06T17:01:37Z
| null |
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I've preprocessed and uploaded a dataset here: https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed . The dataset is in `.arrow` format.
The dataset can correctly be loaded when doing:
```bash
git lfs install
git clone https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed
```
followed by
```python
from datasets import load_from_disk
ds = load_from_disk("./ami_headset_single_preprocessed")
```
However when I try to directly download the dataset as follows:
```python
from datasets import load_dataset
ds = load_dataset("ami-wav2vec2/ami_headset_single_preprocessed")
```
the following error occurs:
```bash
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
724 try:
725 # Prepare split will record examples associated to the split
--> 726 self._prepare_split(split_generator, **prepare_split_kwargs)
727 except OSError as e:
728 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
1186 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1187 ):
-> 1188 writer.write_table(table)
1189 num_examples, num_bytes = writer.finalize()
1190
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_table(self, pa_table, writer_batch_size)
424 # reorder the arrays if necessary + cast to self._schema
425 # we can't simply use .cast here because we may need to change the order of the columns
--> 426 pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
427 batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size)
428 self._num_bytes += sum(batch.nbytes for batch in batches)
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib._sanitize_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
/usr/local/lib/python3.7/dist-packages/pyarrow/compute.py in cast(arr, target_type, safe)
279 else:
280 options = CastOptions.unsafe(target_type)
--> 281 return call_function("cast", [arr], options)
282
283
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list
```
## Expected results
The dataset should be correctly loaded with `load_dataset` IMO.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0
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| 1,016,759,202
|
I_kwDODunzps48moOi
| 3,034
|
Errors loading dataset using fs = a gcsfs.GCSFileSystem
|
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2021-10-05T20:07:08Z
|
2021-10-05T20:26:39Z
| null |
NONE
| null | null | null | null |
## Describe the bug
Cannot load dataset using a `gcsfs.GCSFileSystem`. I'm not sure if this should be a bug in `gcsfs` or here...
Basically what seems to be happening is that since datasets saves datasets as folders and folders aren't "real objects" in gcs, gcsfs raises a 404 error. There are workarounds if you use gcsfs directly to download the file, but as is I can't get `load_from_disk` to work.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# load some dataset
dataset = load_dataset("squad", split="train")
# save it to gcs
import gcsfs
fs = gcsfs.GCSFileSystem(project="my-gs-project")
dataset.save_to_disk("gs://my-bucket/squad", fs=fs)
# try to load it from gcs
from datasets import load_from_disk
dataset2 = load_from_disk("my-bucket/squad", fs=fs)
```
## Expected results
`dataset2` would be a copy of `dataset` but loaded from my bucket.
## Actual results
Long traceback but essentially it's a 404 error from gcsfs saying the object `my-bucket/squad` doesn't exist when this is called:
https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L977
This is because there is no actual object called `my-bucket/squad`, there are objects called `my-bucket/squad/dataset.arrow`, etc.
Note that *this* works fine, since it's explicitly saying "download all the objects with this prefix":
```python
fs.download(src_dataset_path + "/*", dataset_path.as_posix(), recursive=True)
```
For example, I can do a workaround this way:
```python
import tempfile
with tempfile.TemporaryDirectory() as temppath:
fs.download("gs://my-bucket/squad/*", temppath)
dataset2 = load_from_disk(temppath)
```
It's unclear to me if it's `gcsfs`'s responsibility to say "hey that's folder not a file, I should try to get objects inside of it not the object itself", or if that's `datasets`'s responsibility... I'm leaning towards the latter since you're never loading a dataset from one file using this function/method, only a dataset folder?
Another minor thing that should maybe should be rolled into this bug...
https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L968
These fail if you pass in a `gs://` path, e.g.
```python
dataset2 = load_from_disk("gs://my-bucket/squad", fs=fs)
```
Because at this point, `dataset_info_path` is `gs:/my-bucket/squad/dataset_info.json`, gcsfs throws a:
```
Invalid bucket name: 'gs:'
```
error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: macOS Big Sur 11.6
- Python version: 3.7.12
- PyArrow version: 5.0.0
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I_kwDODunzps48lmIb
| 3,032
|
Error when loading private dataset with "data_files" arg
|
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[
"We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !"
] |
2021-10-05T15:46:27Z
|
2021-10-12T15:26:22Z
|
2021-10-12T15:25:46Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
A clear and concise description of what the bug is.
Private datasets with no loading script can't be loaded using `data_files` parameter.
## Steps to reproduce the bug
```python
from datasets import load_dataset
data_files = {"train": "**/train/*/*.jsonl", "valid": "**/valid/*/*.jsonl"}
dataset = load_dataset('dalle-mini/encoded', data_files=data_files, use_auth_token=True, streaming=True)
```
Same error happens in non-streaming mode.
## Expected results
Files should be loaded (whether in streaming or not).
## Actual results
Error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
539 try:
--> 540 local_path = cached_path(file_path, download_config=download_config)
541 except FileNotFoundError:
8 frames
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/dalle-mini/encoded/resolve/main/encoded.py
During handling of the above exception, another exception occurred:
HTTPError Traceback (most recent call last)
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/dalle-mini/encoded?full=true
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
547 except Exception:
548 raise FileNotFoundError(
--> 549 f"Couldn't find a directory or a {resource_type} named '{path}'. "
550 f"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}/datasets"
551 )
FileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini/encoded'. It doesn't exist locally at /content/dalle-mini/encoded or remotely on https://huggingface.co/datasets
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
@lhoestq
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I_kwDODunzps48kThl
| 3,027
|
Resolve data_files by split name
|
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[
"Awesome @lhoestq I like the proposal and it works great on my JSON community dataset. Here is the [log](https://gist.github.com/vblagoje/714babc325bcbdd5de579fd8e1648892). ",
"From my discussion with @borisdayma it would be more general the files match if their paths contains the split name - not only if the filename contains the split name. For example for a dataset like this:\r\n```\r\ntrain/\r\n└── data.csv\r\ntest/\r\n└── data.csv\r\n```\r\n\r\nBut IMO the default should be \r\n```\r\ndata/\r\n├── train.csv\r\n└── test.csv\r\n```\r\nbecause it allows people to have other directories if they have different subsets of their data (different configurations, not splits)",
"I just created a PR for this at https://github.com/huggingface/datasets/pull/3221, let me know what you think :)"
] |
2021-10-05T10:24:36Z
|
2021-11-05T17:49:58Z
|
2021-11-05T17:49:57Z
|
MEMBER
| null | null | null | null |
This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:
```python
load_dataset("lhoestq/demo1")
```
should return two splits "train" and "test" since the dataset repostiory is like
```
data/
├── train.csv
└── test.csv
```
Currently it returns only one split "train" which contains the data of both files
I started playing with this idea on this branch btw: `resolve-data_files-by-split-name`
Basically the idea is that if you named you data files after split names then the default pattern is
```python
{
"train": ["*train*"],
"test": ["*test*"],
"validation": ["*dev*", "valid"],
}
```
otherwise it's
```python
{
"train": ["*"]
}
```
Let me know what you think !
cc @albertvillanova @LysandreJik @vblagoje
|
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I_kwDODunzps48j7yv
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Windows test suite fails
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2021-10-05T08:46:46Z
|
2021-10-05T09:58:27Z
|
2021-10-05T09:58:27Z
|
MEMBER
| null | null | null | null |
## Describe the bug
There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206
```
ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```
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I_kwDODunzps48hG4F
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Support multiple zipped CSV data files
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[
"@lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in `data_files`?",
"`data_dir` is currently exclusively used for manually downloaded data.\r\n\r\nMaybe we can have an API that only uses data_files as you are suggesting, using URL chaining ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https://domain.org/filename.zip\"\r\ndata_files = {\"train\": \"zip://train_filename.csv::\" + url, \"test\": \"zip://test_filename.csv::\" + url}\r\ndataset = load_dataset(\"csv\", data_files=data_files)\r\n```\r\n\r\nURL chaining is used by `fsspec` to get access to files in nested filesystems of any kind. Since `fsspec` is being used by `pandas`, `dask` and also extensively by `datasets` I think it would be nice to use it here too",
"URL chaining sounds super nice to me! And it's also a nice way to leverage the same concepts we currently have in the docs around `fsspec` :)"
] |
2021-10-04T15:16:59Z
|
2021-10-05T14:32:57Z
| null |
MEMBER
| null | null | null | null |
As requested by @lewtun, support loading multiple zipped CSV data files.
```python
from datasets import load_dataset
url = "https://domain.org/filename.zip"
data_files = {"train": "train_filename.csv", "test": "test_filename.csv"}
dataset = load_dataset("csv", data_dir=url, data_files=data_files)
```
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I_kwDODunzps48fxEj
| 3,013
|
Improve `get_dataset_infos`?
|
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[] | null |
[
"To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing."
] |
2021-10-04T09:47:04Z
|
2022-02-21T15:57:10Z
|
2022-02-21T15:57:10Z
|
COLLABORATOR
| null | null | null | null |
Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info:
```
>>> from datasets import get_dataset_infos
>>> get_dataset_infos('wit')
{}
```
While it's totally possible to get it (regenerate it) with:
```
>>> from datasets import load_dataset_builder
>>> builder = load_dataset_builder('wit')
>>> builder.info
DatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n', citation='@article{srinivasan2021wit,\n title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\n author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\n journal={arXiv preprint arXiv:2103.01913},\n year={2021}\n}\n', homepage='https://github.com/google-research-datasets/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)
```
Should we test if info is empty, and in that case regenerate it? Or always generate it?
|
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I_kwDODunzps48frCh
| 3,011
|
load_dataset_builder should error if "name" does not exist?
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[
"Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps://github.com/huggingface/datasets/blob/ba27ce33bf568374cf23a07669fdd875b5718bc2/src/datasets/builder.py#L391-L397"
] |
2021-10-04T09:20:46Z
|
2022-09-20T13:05:07Z
| null |
COLLABORATOR
| null | null | null | null |
```
import datasets as ds
builder = ds.load_dataset_builder('sent_comp', name="doesnotexist")
builder.info.config_name
```
returns
```
'doesnotexist'
```
Shouldn't it raise an error instead?
For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed)
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I_kwDODunzps48fm1G
| 3,010
|
Chain filtering is leaking
|
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[
"### Update:\r\nI wrote a bit cleaner code snippet (without transforming to json) that can expose leaking.\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = ['ab', 'c', 'df']\r\n\r\nds = datasets.Dataset.from_dict({'col': items})\r\nprint(list(ds))\r\n# > Prints: [{'col': 'ab'}, {'col': 'c'}, {'col': 'df'}]\r\n\r\nfiltered = ds\r\n\r\n# get all items that are starting with a character with ascii code bigger than 'a'\r\nfiltered = filtered.filter(lambda x: x['col'][0] > 'a', load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'c'}, {'col': 'df'}] as expected\r\n\r\n# get all items that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(x['col']) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'ab'}] -> this is a leaked item from the first filter\r\n# > Should be: [{'col': 'c'}]\r\n```",
"Thanks for reporting. I'm looking into it",
"I just pushed a fix ! We'll do a new release soon.\r\nIn the meantime feel free to install `datasets` from source to play with it",
"Thanks, I'm already using it from your branch!"
] |
2021-10-04T09:04:55Z
|
2022-06-01T17:36:44Z
|
2022-06-01T17:36:44Z
|
NONE
| null | null | null | null |
## Describe the bug
As there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.
These samples show that filtering behavior diverges from what's expected when chaining filterings.
On sample 2 the second filtering leads to "leaking" of data that should've been filtered on the first filtering into the results.
## Steps to reproduce the bug
Sample 1:
```python
import datasets
import json
items = [[1, 2], [3], [4]]
jsoned_items = map(json.dumps, [[1, 2], [3], [4]])
ds = datasets.Dataset.from_dict({'a': jsoned_items})
print(list(ds))
# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected
filtered = ds
# get all lists that are shorter than 2
filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected
# get all lists, which have a value bigger than 3 on its zero index
filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)
print(list(filtered))
# > Should be: [{'a': [4]}]
# > Prints: [{'a': [3]}]
```
Sample 2:
```python
import datasets
import json
items = [[1, 2], [3], [4]]
jsoned_items = map(json.dumps, [[1, 2], [3], [4]])
ds = datasets.Dataset.from_dict({'a': jsoned_items})
print(list(ds))
# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]
filtered = ds
# get all lists, which have a value bigger than 3 on its zero index
filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[4]'}] as expected
# get all lists that are shorter than 2
filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[1, 2]'}]
# > Should be: [{'a': '[4]'}] (remain intact)
```
## Expected results
Expected and actual results are attached to the code snippets.
## Actual results
Expected and actual results are attached to the code snippets.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.9.7
- PyArrow version: 5.0.0
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DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument
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[
"Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```",
"Thanks, sorry for bothering"
] |
2021-10-04T00:49:29Z
|
2021-10-11T10:18:01Z
|
2021-10-04T08:46:13Z
|
NONE
| null | null | null | null |
## Describe the bug
The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument
## Steps to reproduce the bug
```python
import datasets
example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}})
def filter_value(example, value):
return example['a'] == value
filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
```
## Expected results
`filtered` is a dataset containing {"a": {3}}
## Actual results
> Traceback (most recent call last):
> File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module>
> filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
> out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
> out = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter
> indices = self.map(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map
> return self._map_single(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
> out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
> out = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single
> batch = apply_function_on_filtered_inputs(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs
> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
> TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.9.7
- PyArrow version: 5.0.0
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I_kwDODunzps48ZtfH
| 2,998
|
cannot shuffle dataset loaded from disk
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2021-10-01T13:49:52Z
|
2021-10-01T13:49:52Z
| null |
NONE
| null | null | null | null |
## Describe the bug
dataset loaded from disk cannot be shuffled.
## Steps to reproduce the bug
```
my_dataset = load_from_disk('s3://my_file/validate', fs=s3)
sample = my_dataset.select(range(100)).shuffle(seed=1234)
```
## Actual results
```
sample = my_dataset .select(range(100)).shuffle(seed=1234)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2494, in shuffle
new_fingerprint=new_fingerprint,
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2303, in select
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 547, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 258, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpnnu5uhnx/my_file/validate/tmpy76d70g4'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Python version: 3.7
- PyArrow version: 5.0.0
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I_kwDODunzps48ZUY1
| 2,997
|
Dataset has incorrect labels
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[] | null |
[
"Hi @marshmellow77, thanks for reporting.\r\n\r\nThat issue is fixed since `datasets` version 1.9.0 (see 16bc665f2753677c765011ef79c84e55486d4347).\r\n\r\nPlease, update `datasets` with: `pip install -U datasets`",
"Thanks. Please note that the dataset explorer (https://huggingface.co/datasets/viewer/?dataset=turkish_product_reviews) still shows the incorrect state. The sentiment for the first few customer reviews is actually negative and should be labelled with \"0\", see screenshot:\r\n\r\n\r\n\r\n\r\n",
"Thanks @marshmellow77, good catch! I'm transferring this issue to https://github.com/huggingface/datasets-viewer. "
] |
2021-10-01T12:09:06Z
|
2021-10-01T15:32:00Z
|
2021-10-01T13:54:34Z
|
NONE
| null | null | null | null |
The dataset https://huggingface.co/datasets/turkish_product_reviews has incorrect labels - all reviews are labelled with "1" (positive sentiment). None of the reviews is labelled with "0". See screenshot attached:
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Can't download `trivia_qa/unfiltered`
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"wooo that was fast! thank you @lhoestq !\r\nit is able to process now, though it's ignoring all files and ending up with 0 examples now haha :/\r\n\r\nFor subset \"unfiltered\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1354.53it/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 40.60it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py\", line 1198, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 647, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 748, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=2906575347, num_examples=10832, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=3038966234, num_examples=11313, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\nFor subset \"rc\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"rc\")\r\nDownloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/rc/1.1.0/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3806.08it/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 51.57it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py\", line 1198, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 647, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 748, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=1577814583, num_examples=17210, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='train', num_bytes=12750976012, num_examples=138384, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=1688535379, num_examples=18669, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\n\r\nCould you look into that when you get a chance?\r\nI wonder if it's not something they changed on the file to download? i couldn't find any information",
"@VictorSanh have you tried passing `download_mode=\"force_redownload\"`?\r\n```python\r\nds = load_dataset(\"trivia_qa\", \"unfiltered\", download_mode=\"force_redownload\")\r\n```",
"I aggressively rmed caches, especially rming the `datasets/downloads/extracted/c3d265fa20d99a147a76e4f5e...` solved the issue.\r\nthank you both!\r\n"
] |
2021-09-30T23:00:18Z
|
2021-10-01T19:07:23Z
|
2021-10-01T19:07:22Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
For some reason, I can't download `trivia_qa/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough...
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("trivia_qa", "unfiltered")
Downloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6...
Traceback (most recent call last):
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 251, in _add_context
with open(os.path.join(file_dir, fname), encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/gpfsscratch/rech/six/commun/datasets/downloads/extracted/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad/evidence/wikipedia/Peanuts.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset
use_auth_token=use_auth_token,
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 303, in _generate_examples
example = parse_example(article)
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 274, in parse_example
_add_context(article.get("EntityPages", []), "WikiContext", wiki_dir),
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 253, in _add_context
except (IOError, datasets.Value("errors").NotFoundError):
File "<string>", line 5, in __init__
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 265, in __post_init__
self.pa_type = string_to_arrow(self.dtype)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 134, in string_to_arrow
f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. "
ValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
```
## Expected results
I am able to load another subset (`rc`), but unable to load.
I am not sure why the try/except doesn't catch it...
https://github.com/huggingface/datasets/blob/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/datasets/trivia_qa/trivia_qa.py#L253
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa
- Python version: 3.7.10
- PyArrow version: 3.0.0
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| 1,012,174,823
|
I_kwDODunzps48VI_n
| 2,991
|
add docmentation for the `Unix style pattern` matching feature that can be leverage for `data_files` into `load_dataset`
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2021-09-30T13:22:01Z
|
2021-09-30T13:22:01Z
| null |
CONTRIBUTOR
| null | null | null | null |
Unless I'm mistaken, it seems that in the new documentation it is no longer mentioned that you can use Unix style pattern matching in the `data_files` argument of the `load_dataset` method.
This feature was mentioned [here](https://huggingface.co/docs/datasets/loading_datasets.html#from-a-community-dataset-on-the-hugging-face-hub) in the previous documentation.
I'd love to hear your opinion @lhoestq , @albertvillanova and @stevhliu
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I_kwDODunzps48ROTx
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IndexError: Invalid key: 14 is out of bounds for size 0
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[
"Hi ! Could you check the length of the `self.dataset` object (i.e. the Dataset object passed to the data loader) ? It looks like the dataset is empty.\r\nNot sure why the SWA optimizer would cause this though.",
"Any updates on this? \r\nThe same error occurred to me too when running `cardiffnlp/twitter-roberta-base-sentiment` on a custom dataset. This happened when I tried to do `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])` without using sagemaker distribution. \r\nPython: 3.6.13\r\ndatasets: 1.6.2",
"Hi @ruisi-su, do you have this issue while using SWA as well, or just data parallel ?\r\n\r\nIf you have a code example to reproduce this issue it would also be helpful",
"@lhoestq I had this issue without SWA. I followed [this](https://github.com/huggingface/notebooks/blob/master/sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb) notebook to utilize multiple gpus on the [roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) model. This tutorial could only work if I am on `ml.p3.16xlarge`, which I don't have access to. So I tried using just `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3]` before calling `trainer.fit()`. But maybe this is not the right way to do distributed training. I can provide a code example if that will be more helpful.",
"It might be an issue with old versions of `datasets`, can you try updating `datasets` ?",
"FYI I encountered the exact same error using the latest versions of `datasets`, `transformers` and `pyarrow`, without using any kind of SWA or dataparallel: \r\n\r\n```\r\n# packages in environment at C:\\Users\\zhang\\mambaforge:\r\n#\r\n# Name Version Build Channel\r\ncudatoolkit 11.0.3 h3f58a73_9 https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge\r\ndatasets 1.17.0 pypi_0 pypi\r\npyarrow 6.0.1 pypi_0 pypi\r\npytorch 1.7.1 py3.9_cuda110_cudnn8_0 pytorch\r\ntornado 6.1 py39hb82d6ee_2 https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge\r\n```\r\n\r\n```\r\n> python --version\r\n> 3.9.7\r\n```",
"Same error here! Datasets version `1.18.3` freshly updated.\r\n\r\n`IndexError: Invalid key: 90 is out of bounds for size 0`\r\n\r\nMy task is finetuning the model for token classification.\r\n\r\n**Solved**: I make a mistake while updating the dataset during the map, you should check that you return the correct values.\r\n",
"cc @sgugger This probably comes from the `Trainer` removing all the columns of a dataset, do you think we can improve the error message in this case ?",
"The `Trainer` clearly logs when it removes columns in the dataset. I'm not too sure of where the bug appears as I haven't seen a clear reproducer. Happy to display a more helpful error message, but I'd need a reproducer to see what the exact problem is to design the right test and warning :-) ",
"Well, if I can try to suggest how to reproduce, please try by do not returning any updated content in the map function used to tokenize input (e.g., in TokenClassification). I can leave here my wrong version for reference:\r\n\r\n```python\r\ndef preprocess_function(examples):\r\n\r\n text = examples[\"text\"]\r\n \r\n inputs = tokenizer(\r\n text,\r\n max_length=512,\r\n truncation=\"only_second\",\r\n return_offsets_mapping=True,\r\n padding=\"max_length\",\r\n )\r\n\r\n offset_mapping = inputs.pop(\"offset_mapping\")\r\n # ... processing code\r\n\r\n inputs[\"labels\"] = label_ids\r\n #return inputs\r\n \r\ntrain_ds = train_ds.map(preprocess_function, batched=False)\r\ntest_ds = test_ds.map(preprocess_function, batched=False)\r\neval_ds = eval_ds.map(preprocess_function, batched=False)\r\n```\r\n\r\nOf course, returning inputs solved the problem. As suggestion, a possible error message could display \"IndexError: the `key` required by trainer are not found in the dataset\" (just an hypothesis, I think there could be something better). \r\n\r\nPlease tell me if you need more details to reproduce, glad to help!",
"That's the thing though. The `Trainer` has no idea which inputs are required or not since all models can have different kinds of inputs, and it can work for models outside of the Transformers library. I can add a clear error message if I get an empty batch, as this is easy to detect, but that's pretty much it.",
"I think that it could be enough to ease the identification of the problem.",
"Done in [this commit](https://github.com/huggingface/transformers/commit/c87cfd653c4de3d4743a9ae09d749282d94d5829)"
] |
2021-09-29T16:04:24Z
|
2022-04-10T14:49:49Z
|
2022-04-10T14:49:49Z
|
NONE
| null | null | null | null |
## Describe the bug
A clear and concise description of what the bug is.
Hi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error, since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq
## Steps to reproduce the bug
```
Traceback (most recent call last):
File "run_clm.py", line 723, in <module>
main()
File "run_clm.py", line 669, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train
for step, inputs in enumerate(epoch_iterator):
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1530, in __getitem__
format_kwargs=self._format_kwargs,
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1517, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 368, in query_table
_check_valid_index_key(key, size)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 311, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 14 is out of bounds for size 0
```
## Expected results
not getting the index error
## Actual results
Please see the above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
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I_kwDODunzps48Qwjd
| 2,987
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ArrowInvalid: Can only convert 1-dimensional array values
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[
"Hi @NielsRogge, thanks for reporting!\r\n\r\nIn `datasets`, we were handling N-dimensional arrays only when passed as an instance of `np.array`, not when passed as a list of `np.array`s.\r\n\r\nI'm fixing it."
] |
2021-09-29T14:18:52Z
|
2021-10-01T13:57:45Z
|
2021-10-01T13:57:45Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:
```
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
return encoded_inputs
```
```
Full trace:
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-8-0fc3efc6f0c2> in <module>()
27
28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,
---> 29 features=features)
30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,
31 features=features)
13 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1701 new_fingerprint=new_fingerprint,
1702 disable_tqdm=disable_tqdm,
-> 1703 desc=desc,
1704 )
1705 else:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
396 # Call actual function
397
--> 398 out = func(self, *args, **kwargs)
399
400 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2063 writer.write_table(batch)
2064 else:
-> 2065 writer.write_batch(batch)
2066 if update_data and writer is not None:
2067 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
409 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
410 typed_sequence_examples[col] = typed_sequence
--> 411 pa_table = pa.Table.from_pydict(typed_sequence_examples)
412 self.write_table(pa_table, writer_batch_size)
413
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
106 storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)
107 else:
--> 108 storage = pa.array(self.data, type.storage_dtype)
109 out = pa.ExtensionArray.from_storage(type, storage)
110 elif isinstance(self.data, np.ndarray):
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
It can be fixed by adding the following line:
```diff
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
+ encoded_inputs["image"] = np.array(encoded_inputs["image"])
return encoded_inputs
```
However, would be great if this can be fixed within Datasets itself.
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I_kwDODunzps48OsRm
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|
Exceeded maximum rows when reading large files
|
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[
"Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?"
] |
2021-09-29T04:49:22Z
|
2021-10-12T06:05:42Z
|
2021-10-12T06:05:42Z
|
NONE
| null | null | null | null |
## Describe the bug
A clear and concise description of what the bug is.
When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error.
## Steps to reproduce the bug
```python
dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file
```
## Expected results
No error
## Actual results
```
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
134 with open(file, encoding="utf-8") as f:
--> 135 dataset = json.load(f)
136 except json.JSONDecodeError:
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w)
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 2 column 1 (char 20321)
During handling of the above exception, another exception occurred:
ArrowInvalid Traceback (most recent call last)
<ipython-input-20-ab3718a6482f> in <module>
----> 1 dataset = load_dataset('json', data_files=data_files)
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
841
842 # Download and prepare data
--> 843 builder_instance.download_and_prepare(
844 download_config=download_config,
845 download_mode=download_mode,
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
606 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
607 if not downloaded_from_gcs:
--> 608 self._download_and_prepare(
609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
610 )
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
684 try:
685 # Prepare split will record examples associated to the split
--> 686 self._prepare_split(split_generator, **prepare_split_kwargs)
687 except OSError as e:
688 raise OSError(
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1153 generator = self._generate_tables(**split_generator.gen_kwargs)
1154 with ArrowWriter(features=self.info.features, path=fpath) as writer:
-> 1155 for key, table in utils.tqdm(
1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1157 ):
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
135 dataset = json.load(f)
136 except json.JSONDecodeError:
--> 137 raise e
138 raise ValueError(
139 f"Not able to read records in the JSON file at {file}. "
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
114 while True:
115 try:
--> 116 pa_table = paj.read_json(
117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
118 )
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Exceeded maximum rows
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: 3.9
- PyArrow version: 4.0.1
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I_kwDODunzps48MXJK
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OpenSLR 25: ASR data for Amharic, Swahili and Wolof
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[
"Whoever handles this just needs to: \r\n\r\n- [ ] fork the HuggingFace Datasets repo\r\n- [ ] update the [existing dataset script](https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. \r\nAmharic URL: https://www.openslr.org/resources/25/data_readspeech_am.tar.bz2. \r\nSwahili URL: https://www.openslr.org/resources/25/data_broadcastnews_sw.tar.bz2, \r\nWolof URL: https://www.openslr.org/resources/25/data_readspeech_wo.tar.bz2\r\n- [ ] update the [data card](https://github.com/huggingface/datasets/blob/master/datasets/openslr/README.md) to include information about SLR25. There's lots of other examples to draw from. \r\n- [ ] add the appropriate language tags to the data card as well. https://www.w3.org/International/questions/qa-choosing-language-tags, or just use `sw`, `am`, and `wo` for consistency. \r\n- [ ] make a pull request to merge your changes back into HuggingFace's repo",
"... also the example in \"use in datasets library\" should be updated. It currently says \r\n\r\nBut you actually have to specify a subset, e.g. \r\n```python\r\ndataset = load_dataset(\"openslr\", \"SLR32\")\r\n```",
"\r\n\r\n"
] |
2021-09-28T15:04:36Z
|
2021-09-29T17:25:14Z
| null |
CONTRIBUTOR
| null | null | null | null |
## Adding a Dataset
- **Name:** *SLR25*
- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data*
- **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. *
- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/*
- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*
https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.
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| 1,009,634,147
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I_kwDODunzps48Lctj
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ValueError when computing f1 metric with average None
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[
"Hi @asofiaoliveira, thanks for reporting.\r\n\r\nI'm fixing it."
] |
2021-09-28T11:34:53Z
|
2021-10-01T14:17:38Z
|
2021-10-01T14:17:38Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py:
```python
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
).item(),
}
```
Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library
## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("f1")
metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8])
metric.compute(average=None)
```
## Expected results
`array([0.66666667, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ])`
## Actual results
ValueError: can only convert an array of size 1 to a Python scalar
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.5
- PyArrow version: 5.0.0
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I_kwDODunzps48LBML
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Run CI tests against non-production server
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[] | null |
[
"Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?\r\n\r\nLet's ping @Pierrci @julien-c and @n1t0 for their opinion about that",
"@julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https://app.circleci.com/pipelines/github/huggingface/datasets/7843/workflows/fc83fa32-18f5-4dc3-9e2f-ba277ae1af74)\r\n\r\nFor the decision process: be aware that moon-staging does not have persistent repos (they are deleted regularly). as a consequence, **if the moon-staging solution is validated**, you should consider a way to keep the repository that are loaded in tests. These are the ones I found : https://github.com/huggingface/datasets/blob/d488db2f64f312f88f72bbc57a09b7eddb329182/tests/test_load.py and https://github.com/huggingface/datasets/blob/40773111c3e7db8a992fa1c48af32d900a1018d6/tests/test_streaming_download_manager."
] |
2021-09-28T09:41:26Z
|
2021-09-28T15:23:50Z
| null |
MEMBER
| null | null | null | null |
Currently, the CI test suite performs requests to the HF production server.
As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.
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Impossible to load compressed csv
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[
"Hi @Valahaar, thanks for reporting and for your investigation about the source cause.\r\n\r\nYou are right and that commit prevents `pandas` from inferring the compression. On the other hand, @lhoestq did that change to support loading that dataset in streaming mode. \r\n\r\nI'm fixing it."
] |
2021-09-28T07:18:54Z
|
2021-10-01T15:53:16Z
|
2021-10-01T15:53:15Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
It is not possible to load from a compressed csv anymore.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files=['/path/to/csv.bz2'])
```
## Problem and possible solution
This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).
`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).
## Environment info
- `datasets` version: 1.10.0 (and over)
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
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Can't load dataset
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[
"Hi @mskovalova, \r\n\r\nSome datasets have multiple configurations. Therefore, in order to load them, you have to specify both the *dataset name* and the *configuration name*.\r\n\r\nIn the error message you got, you have a usage example:\r\n- To load the 'wikitext-103-raw-v1' configuration of the 'wikitext' dataset, you should use: \r\n ```python\r\n load_dataset('wikitext', 'wikitext-103-raw-v1')\r\n ```\r\n\r\nIn your case, if you would like to load the 'wikitext-2-v1' configuration of the 'wikitext' dataset, please use:\r\n```python\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-v1\")\r\n```",
"Hi, if I want to load the dataset from local file, then how to specify the configuration name?",
"> 嗨，如果我想从本地文件加载数据集，那么如何指定配置名称？\r\n\r\n请问你知道怎么从本地加载了吗 谢谢",
"`raw_datasets = load_dataset(\"/local/path\", \"wikitext-2-v1\", ...)`\r\nI use datasets==2.18.0 "
] |
2021-09-27T21:38:14Z
|
2024-04-08T03:27:29Z
|
2021-09-28T06:53:01Z
|
NONE
| null | null | null | null |
I'm trying to load a wikitext dataset
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext")
```
ValueError: Config name is missing.
Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']
Example of usage:
`load_dataset('wikitext', 'wikitext-103-raw-v1')`.
If I try
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext-2-v1")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py
#### Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)
- Python version: 3.7.12
- PyArrow version: 3.0.0
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I_kwDODunzps48EfDK
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OSError: Not enough disk space.
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[
"Maybe we can change the disk space calculating API from `shutil.disk_usage` to `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```",
"Hi @qqaatw, thanks for reporting.\r\n\r\nCould you please try:\r\n```python\r\ndataset = load_dataset(\"natural_questions\", cache_dir=os.path.abspath(args.dataset_cache_dir))\r\n```",
"@albertvillanova it works! Thanks for your suggestion. Is that a bug of `DownloadConfig`?",
"`DownloadConfig` only sets the location to download the files. On the other hand, `cache_dir` sets the location for both downloading and caching the data. You can find more information here: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory",
"I had encountered the same error when running a command `ds = load_dataset('food101')` in a docker container. The error I got: `OSError: Not enough disk space. Needed: 9.43 GiB (download: 4.65 GiB, generated: 4.77 GiB, post-processed: Unknown size)`\r\n\r\nIn case anyone encountered the same issue, this was my fix:\r\n\r\n```sh\r\n# starting the container (mount project directory onto /app, so that the code and data in my project directory are available in the container)\r\ndocker run -it --rm -v $(pwd):/app my-demo:latest bash\r\n```\r\n\r\n\r\n```python\r\n# other code ...\r\nds = load_dataset('food101', cache_dir=\"/app/data\") # set cache_dir to the absolute path of a directory (e.g. /app/data) that's mounted from the host (MacOS in my case) into the docker container\r\n\r\n# this assumes ./data directory exists in your project folder. If not, create it or point it to any other existing directory where you want to store the cache\r\n```\r\n\r\nThanks @albertvillanova for posting the fix above :-) ",
"import datasets\r\n datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True"
] |
2021-09-27T07:41:22Z
|
2024-12-04T02:56:19Z
|
2021-09-28T06:43:15Z
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space.
The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir
```
## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
cache_dir=os.path.abspath(args.dataset_cache_dir),
resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```
## Expected results
Can download the dataset without an error.
## Actual results
The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
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